Optimal Powerflow Nonlinear Optimization Benchmark
Data Load and Setup Code
This is generic setup code usable for all solver setups. Basically removing some unnecessary untyped dictionaries before getting to the benchmarks.
PRINT_LEVEL = 0
# This is a soft upper limit to the time of each optimization.
# If times go above this, they will halt early
MAX_CPU_TIME = 100.0
# Maximum number of variables in an optimization problem for the benchmark
# Anything with more variables is rejected and not run
# This is for testing. 100 is a good size for running a test of changes
# Should be set to typemax(Int) to run the whole benchmark
SIZE_LIMIT = 10001000import PowerModels
import ConcreteStructs
using BenchmarkTools
using DataFrames
# PowerModels logs through a Memento handler bound to the real stdout; Weave
# captures chunk output on a pipe, so drop the level before any parse_file call.
PowerModels.logger_config!("error")
ConcreteStructs.@concrete struct DataRepresentation
data::Any
ref::Any
var_lookup::Any
var_init::Any
var_lb::Any
var_ub::Any
ref_gen_idxs::Any
lookup_pg::Any
lookup_qg::Any
lookup_va::Any
lookup_vm::Any
lookup_lij::Any
lookup_p_lij::Any
lookup_q_lij::Any
cost_arrs::Any
f_bus::Any
t_bus::Any
ref_bus_idxs::Any
ref_buses_idxs::Any
ref_bus_gens::Any
ref_bus_arcs::Any
ref_branch_idxs::Any
ref_arcs_from::Any
ref_arcs_to::Any
p_idxmap::Any
q_idxmap::Any
bus_pd::Any
bus_qd::Any
bus_gs::Any
bus_bs::Any
br_g::Any
br_b::Any
br_tr::Any
br_ti::Any
br_ttm::Any
br_g_fr::Any
br_b_fr::Any
br_g_to::Any
br_b_to::Any
end
function load_and_setup_data(file_name)
data = PowerModels.parse_file(file_name)
PowerModels.standardize_cost_terms!(data, order = 2)
PowerModels.calc_thermal_limits!(data)
ref = PowerModels.build_ref(data)[:it][:pm][:nw][0]
# Some data munging to type-stable forms
var_lookup = Dict{String, Int}()
var_init = Float64[]
var_lb = Float64[]
var_ub = Float64[]
var_idx = 1
for (i, bus) in ref[:bus]
push!(var_init, 0.0) #va
push!(var_lb, -Inf)
push!(var_ub, Inf)
var_lookup["va_$(i)"] = var_idx
var_idx += 1
push!(var_init, 1.0) #vm
push!(var_lb, bus["vmin"])
push!(var_ub, bus["vmax"])
var_lookup["vm_$(i)"] = var_idx
var_idx += 1
end
for (i, gen) in ref[:gen]
push!(var_init, 0.0) #pg
push!(var_lb, gen["pmin"])
push!(var_ub, gen["pmax"])
var_lookup["pg_$(i)"] = var_idx
var_idx += 1
push!(var_init, 0.0) #qg
push!(var_lb, gen["qmin"])
push!(var_ub, gen["qmax"])
var_lookup["qg_$(i)"] = var_idx
var_idx += 1
end
for (l, i, j) in ref[:arcs]
branch = ref[:branch][l]
push!(var_init, 0.0) #p
push!(var_lb, -branch["rate_a"])
push!(var_ub, branch["rate_a"])
var_lookup["p_$(l)_$(i)_$(j)"] = var_idx
var_idx += 1
push!(var_init, 0.0) #q
push!(var_lb, -branch["rate_a"])
push!(var_ub, branch["rate_a"])
var_lookup["q_$(l)_$(i)_$(j)"] = var_idx
var_idx += 1
end
@assert var_idx == length(var_init)+1
ref_gen_idxs = [i for i in keys(ref[:gen])]
lookup_pg = Dict{Int, Int}()
lookup_qg = Dict{Int, Int}()
lookup_va = Dict{Int, Int}()
lookup_vm = Dict{Int, Int}()
lookup_lij = Tuple{Int, Int, Int}[]
lookup_p_lij = Int[]
lookup_q_lij = Int[]
cost_arrs = Dict{Int, Vector{Float64}}()
for (i, gen) in ref[:gen]
lookup_pg[i] = var_lookup["pg_$(i)"]
lookup_qg[i] = var_lookup["qg_$(i)"]
cost_arrs[i] = gen["cost"]
end
for (i, bus) in ref[:bus]
lookup_va[i] = var_lookup["va_$(i)"]
lookup_vm[i] = var_lookup["vm_$(i)"]
end
for (l, i, j) in ref[:arcs]
push!(lookup_lij, (l, i, j))
push!(lookup_p_lij, var_lookup["p_$(l)_$(i)_$(j)"])
push!(lookup_q_lij, var_lookup["q_$(l)_$(i)_$(j)"])
end
f_bus = Dict{Int, Int}()
t_bus = Dict{Int, Int}()
for (l, branch) in ref[:branch]
f_bus[l] = branch["f_bus"]
t_bus[l] = branch["t_bus"]
end
ref_bus_idxs = [i for i in keys(ref[:bus])]
ref_buses_idxs = [i for i in keys(ref[:ref_buses])]
ref_bus_gens = ref[:bus_gens]
ref_bus_arcs = ref[:bus_arcs]
ref_branch_idxs = [i for i in keys(ref[:branch])]
ref_arcs_from = ref[:arcs_from]
ref_arcs_to = ref[:arcs_to]
p_idxmap = Dict(lookup_lij[i] => lookup_p_lij[i] for i in 1:length(lookup_lij))
q_idxmap = Dict(lookup_lij[i] => lookup_q_lij[i] for i in 1:length(lookup_lij))
bus_pd = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_qd = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_gs = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_bs = Dict(i => 0.0 for (i, bus) in ref[:bus])
for (i, bus) in ref[:bus]
if length(ref[:bus_loads][i]) > 0
bus_pd[i] = sum(ref[:load][l]["pd"] for l in ref[:bus_loads][i])
bus_qd[i] = sum(ref[:load][l]["qd"] for l in ref[:bus_loads][i])
end
if length(ref[:bus_shunts][i]) > 0
bus_gs[i] = sum(ref[:shunt][s]["gs"] for s in ref[:bus_shunts][i])
bus_bs[i] = sum(ref[:shunt][s]["bs"] for s in ref[:bus_shunts][i])
end
end
br_g = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_tr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_ti = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_ttm = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_g_fr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b_fr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_g_to = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b_to = Dict(i => 0.0 for (i, branch) in ref[:branch])
for (i, branch) in ref[:branch]
g, b = PowerModels.calc_branch_y(branch)
tr, ti = PowerModels.calc_branch_t(branch)
br_g[i] = g
br_b[i] = b
br_tr[i] = tr
br_ti[i] = ti
br_ttm[i] = tr^2 + ti^2
br_g_fr[i] = branch["g_fr"]
br_b_fr[i] = branch["b_fr"]
br_g_to[i] = branch["g_to"]
br_b_to[i] = branch["b_to"]
end
DataRepresentation(
data,
ref,
var_lookup,
var_init,
var_lb,
var_ub,
ref_gen_idxs,
lookup_pg,
lookup_qg,
lookup_va,
lookup_vm,
lookup_lij,
lookup_p_lij,
lookup_q_lij,
cost_arrs,
f_bus,
t_bus,
ref_bus_idxs,
ref_buses_idxs,
ref_bus_gens,
ref_bus_arcs,
ref_branch_idxs,
ref_arcs_from,
ref_arcs_to,
p_idxmap,
q_idxmap,
bus_pd,
bus_qd,
bus_gs,
bus_bs,
br_g,
br_b,
br_tr,
br_ti,
br_ttm,
br_g_fr,
br_b_fr,
br_g_to,
br_b_to)
end
file_name = "../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m"
dataset = load_and_setup_data(file_name);Test Setup
Ensure that all objectives and constraints evaluate to the same value on a feasible point in the same dataset
test_u0 = [0.062436387733897314, 1.0711076238965598, 0.0, 1.066509799068872,
-0.023231313776594726, 1.0879315976617783, -0.033094993289919016,
1.0999999581285527, 0.07121718642320936, 1.094374845084077, 0.4228458440068076,
-3.7102746662566277, 1.8046846767604458e-8, -0.44810504067067086, 8.80063717152151,
-0.0, 0.8709675496332583, 3.6803022758556523, -0.0, 4.618897246588245,
-1.1691336178031877, 1.3748418519024668, 0.9623014391707738, -1.3174990482204871,
-2.3850868109149004, 0.1445158405684026, 2.813869610747349, 0.8151138880859179,
1.9869253829584679, 3.768252275480421, 3.9998421778156934, 0.03553108302190666,
1.177155791026922, -1.3025310027752557, -0.9598988325635542, 1.3193604239530325,
2.399997991458022, -0.003103523654225171, -2.7920689620650667, -0.6047898784636468,
-1.9771521474512397, -3.7071711426024025, -3.9623014391707136, 0.3313990482205271]
test_obj = 16236.704322376236
test_cons = [
0.0, 2.5424107263916085e-14, -1.0835776720341528e-13, -6.039613253960852e-14, 0.0,
0.0, 0.0, -1.7075230118734908e-13, -3.9968028886505635e-14, 1.532107773982716e-13,
0.0, 6.661338147750939e-16, -1.7763568394002505e-15, 0.0, 8.881784197001252e-16,
4.440892098500626e-16, 0.0, 4.440892098500626e-16, -1.7763568394002505e-15,
-8.881784197001252e-16, -4.440892098500626e-16, 2.220446049250313e-16,
0.0, 1.7763568394002505e-15, 0.0, 6.8833827526759706e-15, -8.992806499463768e-15,
3.9968028886505635e-14, -7.105427357601002e-15, 0.0, 0.0, 7.327471962526033e-15,
-7.105427357601002e-15, 2.842170943040401e-14, -7.105427357601002e-15,
-0.033094993289919016, 0.00986367951332429, -0.062436387733897314,
0.07121718642320936, 0.008780798689312044, 0.09444850019980408,
3.2570635340201743, 2.661827801892032, 5.709523923775402, 8.58227283683798,
18.147597689108025, 15.999999905294098, 3.0822827695389314, 2.6621176970504,
5.759999990861611, 8.161419886019171, 17.65224849471505, 15.80965802401578]53-element Vector{Float64}:
0.0
2.5424107263916085e-14
-1.0835776720341528e-13
-6.039613253960852e-14
0.0
0.0
0.0
-1.7075230118734908e-13
-3.9968028886505635e-14
1.532107773982716e-13
⋮
8.58227283683798
18.147597689108025
15.999999905294098
3.0822827695389314
2.6621176970504
5.759999990861611
8.161419886019171
17.65224849471505
15.80965802401578Setup and Validations
Now is the setup code for each optimization framework, along with the validation runs on the test case. Any test which fails the validation case, i.e. has x_test_res[1] !≈ test_obj or x_test_res[2] !≈ test_cons should be considered invalidated as this means that the model in that modeling platform does not evaluate to give the same results
Optimization.jl
Constraint optimization implementation reference: https://github.com/SciML/Optimization.jl/blob/master/lib/OptimizationMOI/test/runtests.jl Other AD libraries can be considered: https://docs.sciml.ai/dev/modules/Optimization/API/optimization_function/
import Optimization
import OptimizationMOI
import ModelingToolkit
import Ipopt
import Enzyme
import ReverseDiff
function build_opf_optimization_prob(dataset; adchoice = Optimization.AutoEnzyme())
(; data,
ref,
var_lookup,
var_init,
var_lb,
var_ub,
ref_gen_idxs,
lookup_pg,
lookup_qg,
lookup_va,
lookup_vm,
lookup_lij,
lookup_p_lij,
lookup_q_lij,
cost_arrs,
f_bus,
t_bus,
ref_bus_idxs,
ref_buses_idxs,
ref_bus_gens,
ref_bus_arcs,
ref_branch_idxs,
ref_arcs_from,
ref_arcs_to,
p_idxmap,
q_idxmap,
bus_pd,
bus_qd,
bus_gs,
bus_bs,
br_g,
br_b,
br_tr,
br_ti,
br_ttm,
br_g_fr,
br_b_fr,
br_g_to,
br_b_to) = dataset
#total_callback_time = 0.0
function opf_objective(x, param)
#start = time()
cost = 0.0
for i in ref_gen_idxs
pg = x[lookup_pg[i]]
_cost_arr = cost_arrs[i]
cost += _cost_arr[1]*pg^2 + _cost_arr[2]*pg + _cost_arr[3]
end
#total_callback_time += time() - start
return cost
end
function opf_constraints(ret, x, param)
offsetidx = 0
# va_con
for (reti, i) in enumerate(ref_buses_idxs)
ret[reti + offsetidx] = x[lookup_va[i]]
end
offsetidx += length(ref_buses_idxs)
# @constraint(model,
# sum(p[a] for a in ref[:bus_arcs][i]) ==
# sum(pg[g] for g in ref_bus_gens[i]) -
# sum(load["pd"] for load in bus_loads) -
# sum(shunt["gs"] for shunt in bus_shunts)*x[lookup_vm[i]]^2
# )
# power_balance_p_con
for (reti, i) in enumerate(ref_bus_idxs)
ret[reti + offsetidx] = sum(x[lookup_pg[j]] for j in ref_bus_gens[i]; init = 0.0) -
bus_pd[i] -
bus_gs[i]*x[lookup_vm[i]]^2 -
sum(x[p_idxmap[a]] for a in ref_bus_arcs[i])
end
offsetidx += length(ref_bus_idxs)
# @constraint(model,
# sum(q[a] for a in ref[:bus_arcs][i]) ==
# sum(x[lookup_qg[g]] for g in ref_bus_gens[i]) -
# sum(load["qd"] for load in bus_loads) +
# sum(shunt["bs"] for shunt in bus_shunts)*x[lookup_vm[i]]^2
# )
# power_balance_q_con
for (reti, i) in enumerate(ref_bus_idxs)
ret[reti + offsetidx] = sum(x[lookup_qg[j]] for j in ref_bus_gens[i]; init = 0.0) -
bus_qd[i] +
bus_bs[i]*x[lookup_vm[i]]^2 -
sum(x[q_idxmap[a]] for a in ref_bus_arcs[i])
end
offsetidx += length(ref_bus_idxs)
# @NLconstraint(model, p_fr == (g+g_fr)/ttm*vm_fr^2 + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-b*tr-g*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to)) )
# power_flow_p_from_con =
for (reti, (l, i, j)) in enumerate(ref_arcs_from)
ret[reti + offsetidx] = (br_g[l]+br_g_fr[l])/br_ttm[l]*x[lookup_vm[f_bus[l]]]^2 +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[f_bus[l]]]*x[lookup_vm[t_bus[l]]]*cos(x[lookup_va[f_bus[l]]]-x[lookup_va[t_bus[l]]])) +
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[f_bus[l]]]*x[lookup_vm[t_bus[l]]]*sin(x[lookup_va[f_bus[l]]]-x[lookup_va[t_bus[l]]])) -
x[p_idxmap[(l, i, j)]]
end
offsetidx += length(ref_arcs_from)
# @NLconstraint(model, p_to == (g+g_to)*vm_to^2 + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-b*tr+g*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr)) )
# power_flow_p_to_con
for (reti, (l, i, j)) in enumerate(ref_arcs_to)
ret[reti + offsetidx] = (br_g[l]+br_g_to[l])*x[lookup_vm[t_bus[l]]]^2 +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[t_bus[l]]]*x[lookup_vm[f_bus[l]]]*cos(x[lookup_va[t_bus[l]]]-x[lookup_va[f_bus[l]]])) +
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[t_bus[l]]]*x[lookup_vm[f_bus[l]]]*sin(x[lookup_va[t_bus[l]]]-x[lookup_va[f_bus[l]]])) -
x[p_idxmap[(l, i, j)]]
end
offsetidx += length(ref_arcs_to)
# @NLconstraint(model, q_fr == -(b+b_fr)/ttm*vm_fr^2 - (-b*tr-g*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to)) )
# power_flow_q_from_con
for (reti, (l, i, j)) in enumerate(ref_arcs_from)
ret[reti + offsetidx] = -(br_b[l]+br_b_fr[l])/br_ttm[l]*x[lookup_vm[f_bus[l]]]^2 -
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[f_bus[l]]]*x[lookup_vm[t_bus[l]]]*cos(x[lookup_va[f_bus[l]]]-x[lookup_va[t_bus[l]]])) +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[f_bus[l]]]*x[lookup_vm[t_bus[l]]]*sin(x[lookup_va[f_bus[l]]]-x[lookup_va[t_bus[l]]])) -
x[q_idxmap[(l, i, j)]]
end
offsetidx += length(ref_arcs_from)
# @NLconstraint(model, q_to == -(b+b_to)*vm_to^2 - (-b*tr+g*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr)) )
# power_flow_q_to_con
for (reti, (l, i, j)) in enumerate(ref_arcs_to)
ret[reti + offsetidx] = -(br_b[l]+br_b_to[l])*x[lookup_vm[t_bus[l]]]^2 -
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[t_bus[l]]]*x[lookup_vm[f_bus[l]]]*cos(x[lookup_va[t_bus[l]]]-x[lookup_va[f_bus[l]]])) +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(x[lookup_vm[t_bus[l]]]*x[lookup_vm[f_bus[l]]]*sin(x[lookup_va[t_bus[l]]]-x[lookup_va[f_bus[l]]])) -
x[q_idxmap[(l, i, j)]]
end
offsetidx += length(ref_arcs_to)
# @constraint(model, va_fr - va_to <= branch["angmax"])
# @constraint(model, va_fr - va_to >= branch["angmin"])
# power_flow_vad_con
for (reti, (l, i, j)) in enumerate(ref_arcs_from)
ret[reti + offsetidx] = x[lookup_va[f_bus[l]]] - x[lookup_va[t_bus[l]]]
end
offsetidx += length(ref_arcs_from)
# @constraint(model, p_fr^2 + q_fr^2 <= branch["rate_a"]^2)
# power_flow_mva_from_con
for (reti, (l, i, j)) in enumerate(ref_arcs_from)
ret[reti + offsetidx] = x[p_idxmap[(l, i, j)]]^2 + x[q_idxmap[(l, i, j)]]^2
end
offsetidx += length(ref_arcs_from)
# @constraint(model, p_to^2 + q_to^2 <= branch["rate_a"]^2)
# power_flow_mva_to_con
for (reti, (l, i, j)) in enumerate(ref_arcs_to)
ret[reti + offsetidx] = x[p_idxmap[(l, i, j)]]^2 + x[q_idxmap[(l, i, j)]]^2
end
offsetidx += length(ref_arcs_to)
@assert offsetidx == length(ret)
nothing
end
con_lbs = Float64[]
con_ubs = Float64[]
#@constraint(model, va[i] == 0)
for (i, bus) in ref[:ref_buses]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_balance_p_con
for (i, bus) in ref[:bus]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_balance_q_con
for (i, bus) in ref[:bus]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_p_from_con
for (l, i, j) in ref[:arcs_from]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_p_to_con
for (l, i, j) in ref[:arcs_to]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_q_from_con
for (l, i, j) in ref[:arcs_from]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_q_to_con
for (l, i, j) in ref[:arcs_to]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_vad_con
for (l, i, j) in ref[:arcs_from]
branch = ref[:branch][l]
push!(con_lbs, branch["angmin"])
push!(con_ubs, branch["angmax"])
end
#power_flow_mva_from_con
for (l, i, j) in ref[:arcs_from]
branch = ref[:branch][l]
push!(con_lbs, -Inf)
push!(con_ubs, branch["rate_a"]^2)
end
#power_flow_mva_to_con
for (l, i, j) in ref[:arcs_to]
branch = ref[:branch][l]
push!(con_lbs, -Inf)
push!(con_ubs, branch["rate_a"]^2)
end
model_variables = length(var_init)
ret = Array{Float64}(undef, length(con_lbs))
model_constraints = length(con_lbs)
optf = Optimization.OptimizationFunction(opf_objective, adchoice; cons = opf_constraints)
prob = Optimization.OptimizationProblem(
optf, var_init; lb = var_lb, ub = var_ub, lcons = con_lbs, ucons = con_ubs)
end
function solve_opf_optimization(dataset; adchoice = Optimization.AutoSparse(Optimization.AutoReverseDiff(true)))
model_build_time = @elapsed prob = build_opf_optimization_prob(dataset; adchoice)
# Correctness tests
ret = zeros(length(prob.lcons))
prob.f.cons(ret, prob.u0, nothing)
@allocated prob.f(prob.u0, nothing) == 0
@allocated prob.f.cons(ret, prob.u0, nothing) == 0
solve_time_with_compilation = @elapsed sol = Optimization.solve(
prob, Ipopt.Optimizer(), print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME)
cost = sol.objective
feasible = (sol.retcode == Optimization.SciMLBase.ReturnCode.Success)
#println(sol.u) # solution vector
solve_time_without_compilation = @elapsed sol = Optimization.solve(
prob, Ipopt.Optimizer(), print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME)
return (prob, sol),
Dict(
"case" => file_name,
"variables" => length(prob.u0),
"constraints" => length(prob.lcons),
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation
)
end
function test_optimization_prob(dataset, test_u0)
prob = build_opf_optimization_prob(dataset)
ret = zeros(length(prob.lcons))
prob.f.cons(ret, test_u0, nothing)
obj = prob.f(test_u0, nothing)
obj, ret
endtest_optimization_prob (generic function with 1 method)optimization_test_res = test_optimization_prob(dataset, test_u0)(16236.704322376236, [0.0, 2.5424107263916085e-14, -1.0835776720341528e-13,
-6.039613253960852e-14, 0.0, 0.0, 0.0, -1.7075230118734908e-13, -3.9968028
886505635e-14, 1.532107773982716e-13 … 5.709523923775402, 8.5822728368379
8, 18.147597689108025, 15.999999905294098, 3.0822827695389314, 2.6621176970
504, 5.759999990861611, 8.161419886019171, 17.65224849471505, 15.8096580240
1578])@assert optimization_test_res[1] == test_obj@assert optimization_test_res[2] == test_consModelingToolkit.jl
Showcases symbolic interface to Optimization.jl, through ModelingToolkit.jl. The simplification process in ModelingToolkit.jl transforms the system to solve for a smaller subset of variables. As a result, while the optimization problem being solved is equivalent constraint function values don't match. The test for this system is thus modified. The test_u0 vector will be appropriately subsetted to match the reduced set of variables, and the resultant point is ensured to be feasible as per the modified constraints. The subsetted point will be used to re-generate the full point using the observed equations in the reduced system, and this point will be validated to match test_u0.
import PowerModels
import Ipopt
using ModelingToolkit, Optimization, OptimizationMOI
import ModelingToolkit: ≲, unknowns
import SymbolicIndexingInterface
using SymbolicIndexingInterface: variable_symbols, all_variable_symbols, getname
function build_opf_mtk_prob(dataset)
(; data, ref) = dataset
vars = Num[]
lb = Float64[]
ub = Float64[]
ModelingToolkit.@variables va[1:maximum(keys(ref[:bus]))]
for i in keys(ref[:bus])
push!(lb, -Inf)
push!(ub, Inf)
end
ModelingToolkit.@variables vm[1:maximum(keys(ref[:bus]))]
for i in keys(ref[:bus])
push!(lb, ref[:bus][i]["vmin"])
push!(ub, ref[:bus][i]["vmax"])
end
vars = vcat(vars, [va[i] for i in keys(ref[:bus])], [vm[i] for i in keys(ref[:bus])])
ModelingToolkit.@variables pg[1:maximum(keys(ref[:gen]))]
for i in keys(ref[:gen])
push!(lb, ref[:gen][i]["pmin"])
push!(ub, ref[:gen][i]["pmax"])
end
ModelingToolkit.@variables qg[1:maximum(keys(ref[:gen]))]
for i in keys(ref[:gen])
push!(lb, ref[:gen][i]["qmin"])
push!(ub, ref[:gen][i]["qmax"])
end
vars = vcat(vars, [pg[i] for i in keys(ref[:gen])], [qg[i] for i in keys(ref[:gen])])
i_inds, j_inds,
l_inds = maximum(first.(ref[:arcs])), maximum(getindex.(ref[:arcs], Ref(2))),
maximum(last.(ref[:arcs]))
ModelingToolkit.@variables p[1:i_inds, 1:j_inds, 1:l_inds]
ModelingToolkit.@variables q[1:i_inds, 1:j_inds, 1:l_inds]
for (l, i, j) in ref[:arcs]
push!(vars, p[l, i, j])
push!(lb, -ref[:branch][l]["rate_a"])
push!(ub, ref[:branch][l]["rate_a"])
end
for (l, i, j) in ref[:arcs]
push!(vars, q[l, i, j])
push!(lb, -ref[:branch][l]["rate_a"])
push!(ub, ref[:branch][l]["rate_a"])
end
loss = sum(gen["cost"][1] * pg[i]^2 + gen["cost"][2] * pg[i] + gen["cost"][3]
for (i, gen) in ref[:gen])
cons = Array{Union{ModelingToolkit.Equation, ModelingToolkit.Inequality}}([])
for (i, bus) in ref[:ref_buses]
push!(cons, va[i] ~ 0)
end
for (i, bus) in ref[:bus]
bus_loads = [ref[:load][l] for l in ref[:bus_loads][i]]
bus_shunts = [ref[:shunt][s] for s in ref[:bus_shunts][i]]
push!(cons,
sum(p[a...] for a in ref[:bus_arcs][i]) ~
(sum(pg[g] for g in ref[:bus_gens][i]; init = 0.0)) -
(sum(load["pd"] for load in bus_loads; init = 0.0)) -
sum(shunt["gs"] for shunt in bus_shunts; init = 0.0)*vm[i]^2
)
push!(cons,
sum(q[a...] for a in ref[:bus_arcs][i]) ~
(sum(qg[g] for g in ref[:bus_gens][i]; init = 0.0)) -
(sum(load["qd"] for load in bus_loads; init = 0.0))
+
sum(shunt["bs"] for shunt in bus_shunts; init = 0.0)*vm[i]^2
)
end
# Branch power flow physics and limit constraints
for (i, branch) in ref[:branch]
f_idx = (i, branch["f_bus"], branch["t_bus"])
t_idx = (i, branch["t_bus"], branch["f_bus"])
p_fr = p[f_idx...]
q_fr = q[f_idx...]
p_to = p[t_idx...]
q_to = q[t_idx...]
vm_fr = vm[branch["f_bus"]]
vm_to = vm[branch["t_bus"]]
va_fr = va[branch["f_bus"]]
va_to = va[branch["t_bus"]]
g, b = PowerModels.calc_branch_y(branch)
tr, ti = PowerModels.calc_branch_t(branch)
ttm = tr^2 + ti^2
g_fr = branch["g_fr"]
b_fr = branch["b_fr"]
g_to = branch["g_to"]
b_to = branch["b_to"]
# From side of the branch flow
push!(cons,
p_fr ~
(g + g_fr) / ttm * vm_fr^2 +
(-g * tr + b * ti) / ttm * (vm_fr * vm_to * cos(va_fr - va_to)) +
(-b * tr - g * ti) / ttm * (vm_fr * vm_to * sin(va_fr - va_to)))
push!(cons,
q_fr ~
-(b + b_fr) / ttm * vm_fr^2 -
(-b * tr - g * ti) / ttm * (vm_fr * vm_to * cos(va_fr - va_to)) +
(-g * tr + b * ti) / ttm * (vm_fr * vm_to * sin(va_fr - va_to)))
# To side of the branch flow
push!(cons,
p_to ~
(g + g_to) * vm_to^2 +
(-g * tr - b * ti) / ttm * (vm_to * vm_fr * cos(va_to - va_fr)) +
(-b * tr + g * ti) / ttm * (vm_to * vm_fr * sin(va_to - va_fr)))
push!(cons,
q_to ~
-(b + b_to) * vm_to^2 -
(-b * tr + g * ti) / ttm * (vm_to * vm_fr * cos(va_to - va_fr)) +
(-g * tr - b * ti) / ttm * (vm_to * vm_fr * sin(va_to - va_fr)))
# Voltage angle difference limit
push!(cons, va_fr - va_to ≲ branch["angmax"])
push!(cons, branch["angmin"] ≲ va_fr - va_to)
# Apparent power limit, from side and to side
push!(cons, p_fr^2 + q_fr^2 ≲ branch["rate_a"]^2)
push!(cons, p_to^2 + q_to^2 ≲ branch["rate_a"]^2)
end
optsys = ModelingToolkit.OptimizationSystem(
loss, vars, [], constraints = cons, name = :rosetta)
optsys = ModelingToolkit.complete(optsys)
u0map = [Num(k) => 0.0 for k in collect(unknowns(optsys))]
ks = collect(Num.(unknowns(optsys)))
for key in keys(ref[:bus])
ind = findfirst(x -> isequal(x, vm[key]), ks)
if ind !== nothing
u0map[ind] = vm[key] => 1.0
end
end
inds = Int[]
for k in collect(unknowns(optsys))
push!(inds, findall(x -> isequal(x, k), vars)[1])
end
prob = Optimization.OptimizationProblem(
optsys, Dict(u0map), lb = lb[inds], ub = ub[inds], grad = true, hess = true,
cons_j = true, cons_h = true, cons_sparse = true, sparse = true)
end
function solve_opf_mtk(dataset)
model_build_time = @elapsed prob = build_opf_mtk_prob(dataset)
# @assert prob.f(prob.u0, nothing) == 0.0 #MTK with simplification doesn't evaluate the same
ret = zeros(length(prob.lcons))
prob.f.cons(ret, prob.u0, nothing)
@allocated prob.f(prob.u0, nothing) == 0
@allocated prob.f.cons(ret, prob.u0, nothing) == 0
solve_time_with_compilation = @elapsed sol = OptimizationMOI.solve(prob, Ipopt.Optimizer())
solve_time_without_compilation = @elapsed sol = OptimizationMOI.solve(prob, Ipopt.Optimizer())
cost = sol.objective
feasible = (sol.retcode == Optimization.SciMLBase.ReturnCode.Success)
return (prob, sol),
Dict(
"case" => file_name,
"variables" => length(prob.u0),
"constraints" => length(prob.lcons),
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation
)
end
# Given a ModelingToolkit.jl variable, translate it to the corresponding
# name it has in the dataset
function mtk_sym_to_name(sym)
sym = ModelingToolkit.unwrap(sym)
return string(getname(sym)) * "_" * join(ModelingToolkit.arguments(sym)[2:end], "_")
end
function test_mtk_prob(dataset, test_u0)
prob = build_opf_mtk_prob(dataset)
syms_subset = variable_symbols(prob)
syms_names = map(mtk_sym_to_name, syms_subset)
subset_idxs = [dataset.var_lookup[name] for name in syms_names]
sub_u0 = test_u0[subset_idxs]
objective = prob.f(sub_u0, prob.p)
cons_buffer = zeros(length(prob.lcons))
prob.f.cons(cons_buffer, sub_u0)
all_syms = all_variable_symbols(prob)
all_syms_names = map(mtk_sym_to_name, all_syms)
all_syms_idxs = [dataset.var_lookup[name] for name in all_syms_names]
reordered_syms = similar(all_syms)
for (sym, idx) in zip(all_syms, all_syms_idxs)
reordered_syms[idx] = sym
end
reconstructed_u0 = SymbolicIndexingInterface.observed(prob, reordered_syms)(sub_u0, prob.p)
return objective, reconstructed_u0, cons_buffer, prob.lcons, prob.ucons
endtest_mtk_prob (generic function with 1 method)objective, reconstructed_u0, cons_vals, lcons, ucons = test_mtk_prob(dataset, test_u0)(16236.704322376236, [0.062436387733897314, 1.0711076238965598, 0.0, 1.0665
09799068872, -0.023231313776594726, 1.0879315976617783, -0.0330949932899190
16, 1.0999999581285527, 0.07121718642320936, 1.094374845084077 … -0.95989
88325635542, 1.3193604239530325, 2.399997991458022, -0.003103523654225171,
-2.7920689620650667, -0.6047898784636468, -1.9771521474512397, -3.707171142
6024025, -3.9623014391707136, 0.3313990482205271], [0.0, 0.0, 0.0, 0.0, 0.0
, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … -Inf, -Inf, 0.0
, 0.0, 0.0, 0.0, -Inf, -Inf, -Inf, -Inf], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.
0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])@assert isapprox(objective, test_obj)# Some pairs of variables have interchangeable values.
# Either one could be specified in terms of the other, and the choice
# is arbitrary during simplification of the symbolic system.
swappable_pairs = [
[dataset.var_lookup["pg_1"], dataset.var_lookup["pg_2"]],
[dataset.var_lookup["qg_1"], dataset.var_lookup["qg_2"]]
]
# Indexes that must match
non_swappable_idxs = setdiff(eachindex(reconstructed_u0), reduce(vcat, swappable_pairs))
@assert isapprox(reconstructed_u0[non_swappable_idxs], test_u0[non_swappable_idxs])
for (i, j) in swappable_pairs
@assert isapprox(reconstructed_u0[[i, j]], test_u0[[i, j]]) ||
isapprox(reconstructed_u0[[j, i]], test_u0[[i, j]])
end# Test all constraint values
@assert all(isapprox.(lcons, cons_vals, atol = 1e-12) .||
(lcons .<= cons_vals .<= ucons) .|| isapprox.(cons_vals, ucons, atol = 1e-12))JuMP.jl
Implementation reference: https://github.com/lanl-ansi/PowerModelsAnnex.jl/blob/master/src/model/ac-opf.jl Only the built-in AD library is supported
import PowerModels
import Ipopt
import JuMP
function build_opf_jump_prob(dataset)
(; data, ref) = dataset
constraints = Any[]
model = JuMP.Model(Ipopt.Optimizer)
vars = [JuMP.@variable(model, va[i in keys(ref[:bus])]),
JuMP.@variable(model,
ref[:bus][i]["vmin"] <= vm[i in keys(ref[:bus])] <= ref[:bus][i]["vmax"],
start=1.0),
JuMP.@variable(model,
ref[:gen][i]["pmin"] <= pg[i in keys(ref[:gen])] <= ref[:gen][i]["pmax"]),
JuMP.@variable(model,
ref[:gen][i]["qmin"] <= qg[i in keys(ref[:gen])] <= ref[:gen][i]["qmax"]),
JuMP.@variable(model,
-ref[:branch][l]["rate_a"] <= p[(l, i, j) in ref[:arcs]] <=
ref[:branch][l]["rate_a"]),
JuMP.@variable(model,
-ref[:branch][l]["rate_a"] <= q[(l, i, j) in ref[:arcs]] <=
ref[:branch][l]["rate_a"])]
JuMP.@objective(model, Min,
sum(gen["cost"][1]*pg[i]^2 + gen["cost"][2]*pg[i] + gen["cost"][3]
for (i, gen) in ref[:gen]))
for (i, bus) in ref[:ref_buses]
push!(constraints, JuMP.@constraint(model, va[i] == 0))
end
for (i, bus) in ref[:bus]
bus_loads = [ref[:load][l] for l in ref[:bus_loads][i]]
bus_shunts = [ref[:shunt][s] for s in ref[:bus_shunts][i]]
push!(constraints,
JuMP.@constraint(model,
sum(p[a] for a in ref[:bus_arcs][i]) ==
sum(pg[g] for g in ref[:bus_gens][i]) -
sum(load["pd"] for load in bus_loads) -
sum(shunt["gs"] for shunt in bus_shunts)*vm[i]^2))
push!(constraints,
JuMP.@constraint(model,
sum(q[a] for a in ref[:bus_arcs][i]) ==
sum(qg[g] for g in ref[:bus_gens][i]) -
sum(load["qd"] for load in bus_loads) +
sum(shunt["bs"] for shunt in bus_shunts)*vm[i]^2))
end
# Branch power flow physics and limit constraints
for (i, branch) in ref[:branch]
f_idx = (i, branch["f_bus"], branch["t_bus"])
t_idx = (i, branch["t_bus"], branch["f_bus"])
p_fr = p[f_idx]
q_fr = q[f_idx]
p_to = p[t_idx]
q_to = q[t_idx]
vm_fr = vm[branch["f_bus"]]
vm_to = vm[branch["t_bus"]]
va_fr = va[branch["f_bus"]]
va_to = va[branch["t_bus"]]
g, b = PowerModels.calc_branch_y(branch)
tr, ti = PowerModels.calc_branch_t(branch)
ttm = tr^2 + ti^2
g_fr = branch["g_fr"]
b_fr = branch["b_fr"]
g_to = branch["g_to"]
b_to = branch["b_to"]
# From side of the branch flow
push!(constraints,
JuMP.@NLconstraint(model,
p_fr ==
(g+g_fr)/ttm*vm_fr^2 + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) +
(-b*tr-g*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to))))
push!(constraints,
JuMP.@NLconstraint(model,
q_fr ==
-(b+b_fr)/ttm*vm_fr^2 - (-b*tr-g*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) +
(-g*tr+b*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to))))
# To side of the branch flow
push!(constraints,
JuMP.@NLconstraint(model,
p_to ==
(g+g_to)*vm_to^2 + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) +
(-b*tr+g*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr))))
push!(constraints,
JuMP.@NLconstraint(model,
q_to ==
-(b+b_to)*vm_to^2 - (-b*tr+g*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) +
(-g*tr-b*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr))))
# Voltage angle difference limit
push!(constraints, JuMP.@constraint(model,
branch["angmin"] <= va_fr - va_to <= branch["angmax"]))
# Apparent power limit, from side and to side
push!(constraints, JuMP.@constraint(model, p_fr^2 + q_fr^2 <= branch["rate_a"]^2))
push!(constraints, JuMP.@constraint(model, p_to^2 + q_to^2 <= branch["rate_a"]^2))
end
model_variables = JuMP.num_variables(model)
# for consistency with other solvers, skip the variable bounds in the constraint count
non_nl_constraints = sum(JuMP.num_constraints(model, ft, st)
for (ft, st) in JuMP.list_of_constraint_types(model) if ft != JuMP.VariableRef)
model_constraints = JuMP.num_nonlinear_constraints(model) + non_nl_constraints
model, vars, constraints
end
function solve_opf_jump(dataset)
model_build_time = @elapsed model = build_opf_jump_prob(dataset)[1]
JuMP.set_attribute(model, "max_cpu_time", MAX_CPU_TIME)
JuMP.set_attribute(model, "print_level", PRINT_LEVEL)
solve_time_with_compilation = @elapsed JuMP.optimize!(model)
solve_time_without_compilation = @elapsed JuMP.optimize!(model)
cost = JuMP.objective_value(model)
feasible = (JuMP.termination_status(model) == JuMP.LOCALLY_SOLVED)
nlp_block = JuMP.MOI.get(model, JuMP.MOI.NLPBlock())
total_callback_time = nlp_block.evaluator.eval_objective_timer +
nlp_block.evaluator.eval_objective_gradient_timer +
nlp_block.evaluator.eval_constraint_timer +
nlp_block.evaluator.eval_constraint_jacobian_timer +
nlp_block.evaluator.eval_hessian_lagrangian_timer
model_variables = JuMP.num_variables(model)
non_nl_constraints = sum(JuMP.num_constraints(model, ft, st)
for (ft, st) in JuMP.list_of_constraint_types(model) if ft != JuMP.VariableRef)
model_constraints = JuMP.num_nonlinear_constraints(model) + non_nl_constraints
return model,
Dict(
"case" => file_name,
"variables" => model_variables,
"constraints" => model_constraints,
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation
)
end
function test_jump_prob(dataset, test_u0)
model, vars, constraints = build_opf_jump_prob(dataset)
(;
lookup_pg,
lookup_qg,
lookup_va,
lookup_vm,
lookup_lij,
lookup_p_lij,
lookup_q_lij) = dataset
f = JuMP.objective_function(model)
flatvars = reduce(vcat, [reduce(vcat, vars[i]) for i in 1:length(vars)])
point = Dict()
for v in flatvars
varname, varint = split(JuMP.name(v), "[")
idx = if varint[1] == '('
varint = (parse(Int, varint[2]), parse(Int, varint[5]), parse(Int, varint[8]))
if varname == "p"
lookup_p_lij[findfirst(x->x==varint, lookup_lij)]
elseif varname == "q"
lookup_q_lij[findfirst(x->x==varint, lookup_lij)]
else
error("Invalid $varname, $varint")
end
else
varint = parse(Int, varint[1:(end - 1)])
if varname == "va"
lookup_va[varint]
elseif varname == "pg"
lookup_pg[varint]
elseif varname == "qg"
lookup_qg[varint]
elseif varname == "vm"
lookup_vm[varint]
else
error("Invalid $varname, $varint")
end
end
point[v] = test_u0[idx]
end
obj = JuMP.value(x->point[x], f)
# The JuMP assertion error is because JuMP and optimization.jl build different problems. JuMP builds f(x) == a and optimization.jl builds f(x) - a == 0
# Workaround this for consistent evaluation
# It's not a general purpose approach because only some of the Optimization.jl constraints are written as f(x) - a = 0 .
# Others are written as f(x) <= a, like the p_fr^2 + q_fr^2 <= branch["rate_a"]^2 constraints
primal_value(set::JuMP.MOI.EqualTo) = JuMP.MOI.constant(set)
primal_value(set) = 0.0
function primal_value(f, constraint)
object = JuMP.constraint_object(constraint)
fx = JuMP.value(f, object.func)
return fx - primal_value(object.set)
end
function primal_value(f, constraint::JuMP.NonlinearConstraintRef)
return JuMP.value(f, constraint)
end
obj = JuMP.value(x->point[x], f)
cons = [primal_value(x->point[x], c) for c in constraints]
obj, cons
endtest_jump_prob (generic function with 1 method)jump_test_res = test_jump_prob(dataset, test_u0)(16236.704322376236, [0.0, -2.55351295663786e-14, 0.0, 1.0835776720341528e-
13, 1.7075230118734908e-13, 6.039613253960852e-14, 3.9968028886505635e-14,
0.0, -1.5298873279334657e-13, 4.440892098500626e-16 … 0.00878079868931204
4, 18.147597689108025, 17.65224849471505, 0.0, 7.105427357601002e-15, 0.0,
7.105427357601002e-15, 0.09444850019980408, 15.999999905294098, 15.80965802
401578])@assert jump_test_res[1] ≈ test_obj@assert sort(abs.(jump_test_res[2])) ≈ sort(abs.(test_cons))NLPModels.jl
Implementation reference: https://juliasmoothoptimizers.github.io/ADNLPModels.jl/stable/tutorial/ Other AD libraries can be considered: https://juliasmoothoptimizers.github.io/ADNLPModels.jl/stable/
import ADNLPModels
import NLPModelsIpopt
function build_opf_nlpmodels_prob(dataset)
(; data, ref) = dataset
bus_pd = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_qd = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_gs = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_bs = Dict(i => 0.0 for (i, bus) in ref[:bus])
for (i, bus) in ref[:bus]
if length(ref[:bus_loads][i]) > 0
bus_pd[i] = sum(ref[:load][l]["pd"] for l in ref[:bus_loads][i])
bus_qd[i] = sum(ref[:load][l]["qd"] for l in ref[:bus_loads][i])
end
if length(ref[:bus_shunts][i]) > 0
bus_gs[i] = sum(ref[:shunt][s]["gs"] for s in ref[:bus_shunts][i])
bus_bs[i] = sum(ref[:shunt][s]["bs"] for s in ref[:bus_shunts][i])
end
end
br_g = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_tr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_ti = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_ttm = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_g_fr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b_fr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_g_to = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b_to = Dict(i => 0.0 for (i, branch) in ref[:branch])
for (i, branch) in ref[:branch]
g, b = PowerModels.calc_branch_y(branch)
tr, ti = PowerModels.calc_branch_t(branch)
br_g[i] = g
br_b[i] = b
br_tr[i] = tr
br_ti[i] = ti
br_ttm[i] = tr^2 + ti^2
br_g_fr[i] = branch["g_fr"]
br_b_fr[i] = branch["b_fr"]
br_g_to[i] = branch["g_to"]
br_b_to[i] = branch["b_to"]
end
var_lookup = Dict{String, Int}()
var_init = Float64[]
var_lb = Float64[]
var_ub = Float64[]
var_idx = 1
for (i, bus) in ref[:bus]
push!(var_init, 0.0) #va
push!(var_lb, -Inf)
push!(var_ub, Inf)
var_lookup["va_$(i)"] = var_idx
var_idx += 1
push!(var_init, 1.0) #vm
push!(var_lb, bus["vmin"])
push!(var_ub, bus["vmax"])
var_lookup["vm_$(i)"] = var_idx
var_idx += 1
end
for (i, gen) in ref[:gen]
push!(var_init, 0.0) #pg
push!(var_lb, gen["pmin"])
push!(var_ub, gen["pmax"])
var_lookup["pg_$(i)"] = var_idx
var_idx += 1
push!(var_init, 0.0) #qg
push!(var_lb, gen["qmin"])
push!(var_ub, gen["qmax"])
var_lookup["qg_$(i)"] = var_idx
var_idx += 1
end
for (l, i, j) in ref[:arcs]
branch = ref[:branch][l]
push!(var_init, 0.0) #p
push!(var_lb, -branch["rate_a"])
push!(var_ub, branch["rate_a"])
var_lookup["p_$(l)_$(i)_$(j)"] = var_idx
var_idx += 1
push!(var_init, 0.0) #q
push!(var_lb, -branch["rate_a"])
push!(var_ub, branch["rate_a"])
var_lookup["q_$(l)_$(i)_$(j)"] = var_idx
var_idx += 1
end
@assert var_idx == length(var_init)+1
#total_callback_time = 0.0
function opf_objective(x)
#start = time()
cost = 0.0
for (i, gen) in ref[:gen]
pg = x[var_lookup["pg_$(i)"]]
cost += gen["cost"][1]*pg^2 + gen["cost"][2]*pg + gen["cost"][3]
end
#total_callback_time += time() - start
return cost
end
function opf_constraints!(cx, x)
#start = time()
va = Dict(i => x[var_lookup["va_$(i)"]] for (i, bus) in ref[:bus])
vm = Dict(i => x[var_lookup["vm_$(i)"]] for (i, bus) in ref[:bus])
pg = Dict(i => x[var_lookup["pg_$(i)"]] for (i, gen) in ref[:gen])
qg = Dict(i => x[var_lookup["qg_$(i)"]] for (i, gen) in ref[:gen])
p = Dict((l, i, j) => x[var_lookup["p_$(l)_$(i)_$(j)"]] for (l, i, j) in ref[:arcs])
q = Dict((l, i, j) => x[var_lookup["q_$(l)_$(i)_$(j)"]] for (l, i, j) in ref[:arcs])
vm_fr = Dict(l => vm[branch["f_bus"]] for (l, branch) in ref[:branch])
vm_to = Dict(l => vm[branch["t_bus"]] for (l, branch) in ref[:branch])
va_fr = Dict(l => va[branch["f_bus"]] for (l, branch) in ref[:branch])
va_to = Dict(l => va[branch["t_bus"]] for (l, branch) in ref[:branch])
# va_con = [va[i] for (i,bus) in ref[:ref_buses]]
k = 0
for (i, bus) in ref[:ref_buses]
k += 1
cx[k] = va[i]
end
# @constraint(model,
# sum(p[a] for a in ref[:bus_arcs][i]) ==
# sum(pg[g] for g in ref[:bus_gens][i]) -
# sum(load["pd"] for load in bus_loads) -
# sum(shunt["gs"] for shunt in bus_shunts)*vm[i]^2
# )
for (i, bus) in ref[:bus]
k += 1
cx[k] = sum(pg[j] for j in ref[:bus_gens][i]; init = 0.0) - bus_pd[i] -
bus_gs[i]*vm[i]^2 - sum(p[a] for a in ref[:bus_arcs][i])
end
# @constraint(model,
# sum(q[a] for a in ref[:bus_arcs][i]) ==
# sum(qg[g] for g in ref[:bus_gens][i]) -
# sum(load["qd"] for load in bus_loads) +
# sum(shunt["bs"] for shunt in bus_shunts)*vm[i]^2
# )
for (i, bus) in ref[:bus]
k += 1
cx[k] = sum(qg[j] for j in ref[:bus_gens][i]; init = 0.0) - bus_qd[i] +
bus_bs[i]*vm[i]^2 - sum(q[a] for a in ref[:bus_arcs][i])
end
# @NLconstraint(model, p_fr == (g+g_fr)/ttm*vm_fr^2 + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-b*tr-g*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to)) )
for (l, i, j) in ref[:arcs_from]
k += 1
cx[k] = (br_g[l]+br_g_fr[l])/br_ttm[l]*vm_fr[l]^2 +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*cos(va_fr[l]-va_to[l])) +
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*sin(va_fr[l]-va_to[l])) -
p[(l, i, j)]
end
# @NLconstraint(model, p_to == (g+g_to)*vm_to^2 + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-b*tr+g*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr)) )
for (l, i, j) in ref[:arcs_to]
k += 1
cx[k] = (br_g[l]+br_g_to[l])*vm_to[l]^2 +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*cos(va_to[l]-va_fr[l])) +
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*sin(va_to[l]-va_fr[l])) -
p[(l, i, j)]
end
# @NLconstraint(model, q_fr == -(b+b_fr)/ttm*vm_fr^2 - (-b*tr-g*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to)) )
for (l, i, j) in ref[:arcs_from]
k += 1
cx[k] = -(br_b[l]+br_b_fr[l])/br_ttm[l]*vm_fr[l]^2 -
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*cos(va_fr[l]-va_to[l])) +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*sin(va_fr[l]-va_to[l])) -
q[(l, i, j)]
end
# @NLconstraint(model, q_to == -(b+b_to)*vm_to^2 - (-b*tr+g*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr)) )
for (l, i, j) in ref[:arcs_to]
k += 1
cx[k] = -(br_b[l]+br_b_to[l])*vm_to[l]^2 -
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*cos(va_to[l]-va_fr[l])) +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*sin(va_to[l]-va_fr[l])) -
q[(l, i, j)]
end
# @constraint(model, va_fr - va_to <= branch["angmax"])
# @constraint(model, va_fr - va_to >= branch["angmin"])
for (l, i, j) in ref[:arcs_from]
k += 1
cx[k] = va_fr[l] - va_to[l]
end
# @constraint(model, p_fr^2 + q_fr^2 <= branch["rate_a"]^2)
for (l, i, j) in ref[:arcs_from]
k += 1
cx[k] = p[(l, i, j)]^2 + q[(l, i, j)]^2
end
# @constraint(model, p_to^2 + q_to^2 <= branch["rate_a"]^2)
for (l, i, j) in ref[:arcs_to]
k += 1
cx[k] = p[(l, i, j)]^2 + q[(l, i, j)]^2
end
#total_callback_time += time() - start
return cx
end
con_lbs = Float64[]
con_ubs = Float64[]
#@constraint(model, va[i] == 0)
for (i, bus) in ref[:ref_buses]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_balance_p_con
for (i, bus) in ref[:bus]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_balance_q_con
for (i, bus) in ref[:bus]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_p_from_con
for (l, i, j) in ref[:arcs_from]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_p_to_con
for (l, i, j) in ref[:arcs_to]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_q_from_con
for (l, i, j) in ref[:arcs_from]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_q_to_con
for (l, i, j) in ref[:arcs_to]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_vad_con
for (l, i, j) in ref[:arcs_from]
branch = ref[:branch][l]
push!(con_lbs, branch["angmin"])
push!(con_ubs, branch["angmax"])
end
#power_flow_mva_from_con
for (l, i, j) in ref[:arcs_from]
branch = ref[:branch][l]
push!(con_lbs, -Inf)
push!(con_ubs, branch["rate_a"]^2)
end
#power_flow_mva_to_con
for (l, i, j) in ref[:arcs_to]
branch = ref[:branch][l]
push!(con_lbs, -Inf)
push!(con_ubs, branch["rate_a"]^2)
end
model_variables = length(var_init)
model_constraints = length(opf_constraints!(similar(con_lbs), var_init))
#=
backend = ADNLPModels.ADModelBackend(model_variables, opf_objective, model_constraints, opf_constraints!;
gradient_backend = ADNLPModels.ReverseDiffADGradient,
hprod_backend = ADNLPModels.SDTForwardDiffADHvprod,
jprod_backend = ADNLPModels.ForwardDiffADJprod,
jtprod_backend = ADNLPModels.ReverseDiffADJtprod,
jacobian_backend = ADNLPModels.ForwardDiffADJacobian, # SDTSparseADJacobian,
hessian_backend = ADNLPModels.ForwardDiffADHessian, # SparseADJacobian,
ghjvprod_backend = ADNLPModels.ForwardDiffADGHjvprod,
hprod_residual_backend = ADNLPModels.ReverseDiffADHvprod,
jprod_residual_backend = ADNLPModels.ForwardDiffADJprod,
jtprod_residual_backend = ADNLPModels.ReverseDiffADJtprod,
jacobian_residual_backend = ADNLPModels.ForwardDiffADHessian, # SparseADJacobian,
hessian_residual_backend = ADNLPModels.ForwardDiffADHessian
)
=#
nlp = ADNLPModels.ADNLPModel!(opf_objective, var_init, var_lb, var_ub,
opf_constraints!, con_lbs, con_ubs, backend = :optimized)
end
function solve_opf_nlpmodels(dataset)
model_build_time = @elapsed nlp = build_opf_nlpmodels_prob(dataset)
solve_time_with_compilation = @elapsed output = NLPModelsIpopt.ipopt(
nlp, print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME)
solve_time_without_compilation = @elapsed output = NLPModelsIpopt.ipopt(
nlp, print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME)
cost = output.objective
feasible = (output.primal_feas <= 1e-6)
model_variables = nlp.meta.nvar
model_constraints = nlp.meta.ncon
return (nlp, output),
Dict(
"case" => file_name,
"variables" => model_variables,
"constraints" => model_constraints,
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation
)
end
function test_nlpmodels_prob(dataset, test_u0)
nlp = build_opf_nlpmodels_prob(dataset)
ret = zeros(nlp.meta.ncon)
nlp.c!(ret, test_u0)
obj = nlp.f(test_u0)
obj, ret
endtest_nlpmodels_prob (generic function with 1 method)nlpmodels_test_res = test_nlpmodels_prob(dataset, test_u0)(16236.704322376236, [0.0, 2.5424107263916085e-14, -1.0835776720341528e-13,
-6.039613253960852e-14, 0.0, 0.0, 0.0, -1.7075230118734908e-13, -3.9968028
886505635e-14, 1.532107773982716e-13 … 5.709523923775402, 8.5822728368379
8, 18.147597689108025, 15.999999905294098, 3.0822827695389314, 2.6621176970
504, 5.759999990861611, 8.161419886019171, 17.65224849471505, 15.8096580240
1578])@assert nlpmodels_test_res[1] == test_obj@assert nlpmodels_test_res[2] == test_consNonconvex
Implementation reference: https://julianonconvex.github.io/Nonconvex.jl/stable/problem/ Currently does not converge due to an upstream issue with the AD backend Zygote: https://github.com/JuliaNonconvex/Nonconvex.jl/issues/130 Note: Nonconvex.jl is incompatible with Symbolics v7 and has been removed from this benchmark.
#=
import Nonconvex
Nonconvex.@load Ipopt
function build_opf_nonconvex_prob(dataset)
(; data, ref) = dataset
time_model_start = time()
model = Nonconvex.DictModel()
bus_pd = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_qd = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_gs = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_bs = Dict(i => 0.0 for (i, bus) in ref[:bus])
for (i, bus) in ref[:bus]
if length(ref[:bus_loads][i]) > 0
bus_pd[i] = sum(ref[:load][l]["pd"] for l in ref[:bus_loads][i])
bus_qd[i] = sum(ref[:load][l]["qd"] for l in ref[:bus_loads][i])
end
if length(ref[:bus_shunts][i]) > 0
bus_gs[i] = sum(ref[:shunt][s]["gs"] for s in ref[:bus_shunts][i])
bus_bs[i] = sum(ref[:shunt][s]["bs"] for s in ref[:bus_shunts][i])
end
end
br_g = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_tr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_ti = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_ttm = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_g_fr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b_fr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_g_to = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b_to = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_rate_a = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_angmin = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_angmax = Dict(i => 0.0 for (i, branch) in ref[:branch])
for (i, branch) in ref[:branch]
g, b = PowerModels.calc_branch_y(branch)
tr, ti = PowerModels.calc_branch_t(branch)
br_g[i] = g
br_b[i] = b
br_tr[i] = tr
br_ti[i] = ti
br_ttm[i] = tr^2 + ti^2
br_g_fr[i] = branch["g_fr"]
br_b_fr[i] = branch["b_fr"]
br_g_to[i] = branch["g_to"]
br_b_to[i] = branch["b_to"]
br_rate_a[i] = branch["rate_a"]
br_angmin[i] = branch["angmin"]
br_angmax[i] = branch["angmax"]
end
for (i, bus) in ref[:bus]
addvar!(model, "va_$(i)", -Inf, Inf, init = 0.0) #va
addvar!(model, "vm_$(i)", bus["vmin"], bus["vmax"], init = 1.0) #vm
end
for (i, gen) in ref[:gen]
addvar!(model, "pg_$(i)", gen["pmin"], gen["pmax"], init = 0.0) #pg
addvar!(model, "qg_$(i)", gen["qmin"], gen["qmax"], init = 0.0) #qg
end
for (l, i, j) in ref[:arcs]
branch = ref[:branch][l]
addvar!(model, "p_$(l)_$(i)_$(j)", -branch["rate_a"], branch["rate_a"], init = 0.0) #p
addvar!(model, "q_$(l)_$(i)_$(j)", -branch["rate_a"], branch["rate_a"], init = 0.0) #q
end
# JuMP.@objective(model, Min, sum(gen["cost"][1]*pg[i]^2 + gen["cost"][2]*pg[i] + gen["cost"][3] for (i,gen) in ref[:gen]))
function opf_objective(x::OrderedDict)
cost = 0.0
for (i, gen) in ref[:gen]
pg = x["pg_$(i)"]
cost += gen["cost"][1]*pg^2 + gen["cost"][2]*pg + gen["cost"][3]
end
return cost
end
Nonconvex.set_objective!(model, opf_objective)
# JuMP.@constraint(model, va[i] == 0)
function const_ref_bus(x::OrderedDict, i)
return x["va_$(i)"]
end
for (i, bus) in ref[:ref_buses]
add_eq_constraint!(model, x -> const_ref_bus(x, i))
end
# @constraint(model,
# sum(p[a] for a in ref[:bus_arcs][i]) ==
# sum(pg[g] for g in ref[:bus_gens][i]) -
# sum(load["pd"] for load in bus_loads) -
# sum(shunt["gs"] for shunt in bus_shunts)*vm[i]^2
# )
function const_power_balance_p(x::OrderedDict, b)
balance = - bus_pd[b] - bus_gs[b]*x["vm_$(b)"]^2
for (l, i, j) in ref[:bus_arcs][b]
balance -= x["p_$(l)_$(i)_$(j)"]
end
for j in ref[:bus_gens][b]
balance += x["pg_$(j)"]
end
return balance
end
# @constraint(model,
# sum(q[a] for a in ref[:bus_arcs][i]) ==
# sum(qg[g] for g in ref[:bus_gens][i]) -
# sum(load["qd"] for load in bus_loads) +
# sum(shunt["bs"] for shunt in bus_shunts)*vm[i]^2
# )
function const_power_balance_q(x::OrderedDict, b)
balance = - bus_qd[b] + bus_bs[b]*x["vm_$(b)"]^2
for (l, i, j) in ref[:bus_arcs][b]
balance -= x["q_$(l)_$(i)_$(j)"]
end
for j in ref[:bus_gens][b]
balance += x["qg_$(j)"]
end
return balance
end
for (i, bus) in ref[:bus]
add_eq_constraint!(model, x -> const_power_balance_p(x, i))
add_eq_constraint!(model, x -> const_power_balance_q(x, i))
end
# @NLconstraint(model, p_fr == (g+g_fr)/ttm*vm_fr^2 + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-b*tr-g*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to)) )
function const_flow_p_from(x::OrderedDict, l, i, j)
return (br_g[l]+br_g_fr[l])/br_ttm[l]*x["vm_$(i)"]^2 +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(x["vm_$(i)"]*x["vm_$(j)"]*cos(x["va_$(i)"]-x["va_$(j)"])) +
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(x["vm_$(i)"]*x["vm_$(j)"]*sin(x["va_$(i)"]-x["va_$(j)"])) -
x["p_$(l)_$(i)_$(j)"]
end
# @NLconstraint(model, q_fr == -(b+b_fr)/ttm*vm_fr^2 - (-b*tr-g*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to)) )
function const_flow_q_from(x::OrderedDict, l, i, j)
return -(br_b[l]+br_b_fr[l])/br_ttm[l]*x["vm_$(i)"]^2 -
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(x["vm_$(i)"]*x["vm_$(j)"]*cos(x["va_$(i)"]-x["va_$(j)"])) +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(x["vm_$(i)"]*x["vm_$(j)"]*sin(x["va_$(i)"]-x["va_$(j)"])) -
x["q_$(l)_$(i)_$(j)"]
end
# @NLconstraint(model, p_to == (g+g_to)*vm_to^2 + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-b*tr+g*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr)) )
function const_flow_p_to(x::OrderedDict, l, i, j)
return (br_g[l]+br_g_to[l])*x["vm_$(j)"]^2 +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(x["vm_$(j)"]*x["vm_$(i)"]*cos(x["va_$(j)"]-x["va_$(i)"])) +
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(x["vm_$(j)"]*x["vm_$(i)"]*sin(x["va_$(j)"]-x["va_$(i)"])) -
x["p_$(l)_$(j)_$(i)"]
end
# @NLconstraint(model, q_to == -(b+b_to)*vm_to^2 - (-b*tr+g*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr)) )
function const_flow_q_to(x::OrderedDict, l, i, j)
return -(br_b[l]+br_b_to[l])*x["vm_$(j)"]^2 -
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(x["vm_$(j)"]*x["vm_$(i)"]*cos(x["va_$(j)"]-x["va_$(i)"])) +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(x["vm_$(j)"]*x["vm_$(i)"]*sin(x["va_$(j)"]-x["va_$(i)"])) -
x["q_$(l)_$(j)_$(i)"]
end
function const_thermal_limit(x::OrderedDict, l, i, j)
return x["p_$(l)_$(i)_$(j)"]^2 + x["q_$(l)_$(i)_$(j)"]^2 - br_rate_a[l]^2
end
function const_voltage_angle_difference_lb(x::OrderedDict, l, i, j)
return br_angmin[l] - x["va_$(i)"] + x["va_$(j)"]
end
function const_voltage_angle_difference_ub(x::OrderedDict, l, i, j)
return x["va_$(i)"] - x["va_$(j)"] - br_angmax[l]
end
for (l, i, j) in ref[:arcs_from]
add_eq_constraint!(model, x -> const_flow_p_from(x, l, i, j))
add_eq_constraint!(model, x -> const_flow_q_from(x, l, i, j))
add_eq_constraint!(model, x -> const_flow_p_to(x, l, i, j))
add_eq_constraint!(model, x -> const_flow_q_to(x, l, i, j))
add_ineq_constraint!(model, x -> const_thermal_limit(x, l, i, j))
add_ineq_constraint!(model, x -> const_thermal_limit(x, l, j, i))
add_ineq_constraint!(model, x -> const_voltage_angle_difference_lb(x, l, i, j))
add_ineq_constraint!(model, x -> const_voltage_angle_difference_ub(x, l, i, j))
end
model
end
function solve_opf_nonconvex(dataset)
model_build_time = @elapsed model = build_opf_nonconvex_prob(dataset)
solve_time_with_compilation = @elapsed result = Nonconvex.optimize(
model,
IpoptAlg(),
NonconvexCore.getinit(model);
options = IpoptOptions(; first_order = false, symbolic = false, sparse = true,
print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME)
)
solve_time_without_compilation = @elapsed result = Nonconvex.optimize(
model,
IpoptAlg(),
NonconvexCore.getinit(model);
options = IpoptOptions(; first_order = false, symbolic = false, sparse = true,
print_level = PRINT_LEVEL, max_cpu_time = MAX_CPU_TIME)
)
cost = result.minimum
feasible = result.status == 0 # just guessing this is correct for Ipopt
model_variables = Nonconvex.NonconvexCore.getnvars(model)
model_constraints = Nonconvex.NonconvexCore.getnconstraints(model)
return (model, result),
Dict(
"case" => file_name,
"variables" => model_variables,
"constraints" => model_constraints,
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation
)
end
function test_nonconvex_prob(dataset, test_u0)
model = build_opf_nonconvex_prob(dataset)
(;
lookup_pg,
lookup_qg,
lookup_va,
lookup_vm,
lookup_lij,
lookup_p_lij,
lookup_q_lij) = dataset
point = Dict()
for v in keys(model.init)
varsplit = split(v, "_")
varname = varsplit[1]
varint = parse.(Int, varsplit[2:end])
idx = if varname == "p"
lookup_p_lij[findfirst(x->x==Tuple(varint), lookup_lij)]
elseif varname == "q"
lookup_q_lij[findfirst(x->x==Tuple(varint), lookup_lij)]
elseif varname == "va"
lookup_va[varint[1]]
elseif varname == "pg"
lookup_pg[varint[1]]
elseif varname == "qg"
lookup_qg[varint[1]]
elseif varname == "vm"
lookup_vm[varint[1]]
else
error("Invalid $varname, $varint")
end
point[v] = test_u0[idx]
end
u0 = OrderedDict(keys(model.init) .=> getindex.((point,), keys(model.init)))
obj = model.objective(u0)
cons = vcat(model.eq_constraints(u0), model.ineq_constraints(u0))
obj, cons
end
=##= Nonconvex tests disabled - package incompatible with Symbolics v7
nonconvex_test_res = test_nonconvex_prob(dataset, test_u0)
@assert nonconvex_test_res[1] ≈ test_obj
@assert sort(abs.(nonconvex_test_res[2])) ≈ sort(abs.(test_cons))
println(sort(abs.(nonconvex_test_res[2])))
=#println(sort(abs.(test_cons)))[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.220446049250313e-
16, 4.440892098500626e-16, 4.440892098500626e-16, 4.440892098500626e-16, 6.
661338147750939e-16, 8.881784197001252e-16, 8.881784197001252e-16, 1.776356
8394002505e-15, 1.7763568394002505e-15, 1.7763568394002505e-15, 6.883382752
6759706e-15, 7.105427357601002e-15, 7.105427357601002e-15, 7.10542735760100
2e-15, 7.327471962526033e-15, 8.992806499463768e-15, 2.5424107263916085e-14
, 2.842170943040401e-14, 3.9968028886505635e-14, 3.9968028886505635e-14, 6.
039613253960852e-14, 1.0835776720341528e-13, 1.532107773982716e-13, 1.70752
30118734908e-13, 0.008780798689312044, 0.00986367951332429, 0.0330949932899
19016, 0.062436387733897314, 0.07121718642320936, 0.09444850019980408, 2.66
1827801892032, 2.6621176970504, 3.0822827695389314, 3.2570635340201743, 5.7
09523923775402, 5.759999990861611, 8.161419886019171, 8.58227283683798, 15.
80965802401578, 15.999999905294098, 17.65224849471505, 18.147597689108025]Optim.jl
Implementation reference: https://julianlsolvers.github.io/Optim.jl/stable/#examples/generated/ipnewton_basics/ Currently does not converge to a feasible point, root cause in unclear debug/optim-debug.jl can be used to confirm it will converge if given a suitable starting point
import Optim
function build_opf_optim_prob(dataset)
(; data, ref) = dataset
bus_pd = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_qd = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_gs = Dict(i => 0.0 for (i, bus) in ref[:bus])
bus_bs = Dict(i => 0.0 for (i, bus) in ref[:bus])
for (i, bus) in ref[:bus]
if length(ref[:bus_loads][i]) > 0
bus_pd[i] = sum(ref[:load][l]["pd"] for l in ref[:bus_loads][i])
bus_qd[i] = sum(ref[:load][l]["qd"] for l in ref[:bus_loads][i])
end
if length(ref[:bus_shunts][i]) > 0
bus_gs[i] = sum(ref[:shunt][s]["gs"] for s in ref[:bus_shunts][i])
bus_bs[i] = sum(ref[:shunt][s]["bs"] for s in ref[:bus_shunts][i])
end
end
br_g = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_tr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_ti = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_ttm = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_g_fr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b_fr = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_g_to = Dict(i => 0.0 for (i, branch) in ref[:branch])
br_b_to = Dict(i => 0.0 for (i, branch) in ref[:branch])
for (i, branch) in ref[:branch]
g, b = PowerModels.calc_branch_y(branch)
tr, ti = PowerModels.calc_branch_t(branch)
br_g[i] = g
br_b[i] = b
br_tr[i] = tr
br_ti[i] = ti
br_ttm[i] = tr^2 + ti^2
br_g_fr[i] = branch["g_fr"]
br_b_fr[i] = branch["b_fr"]
br_g_to[i] = branch["g_to"]
br_b_to[i] = branch["b_to"]
end
var_lookup = Dict{String, Int}()
var_init = Float64[]
var_lb = Float64[]
var_ub = Float64[]
var_idx = 1
for (i, bus) in ref[:bus]
push!(var_init, 0.0) #va
push!(var_lb, -Inf)
push!(var_ub, Inf)
var_lookup["va_$(i)"] = var_idx
var_idx += 1
push!(var_init, 1.0) #vm
push!(var_lb, bus["vmin"])
push!(var_ub, bus["vmax"])
var_lookup["vm_$(i)"] = var_idx
var_idx += 1
end
for (i, gen) in ref[:gen]
#push!(var_init, 0.0) #pg
push!(var_init, (gen["pmax"]+gen["pmin"])/2) # non-standard start
push!(var_lb, gen["pmin"])
push!(var_ub, gen["pmax"])
var_lookup["pg_$(i)"] = var_idx
var_idx += 1
#push!(var_init, 0.0) #qg
push!(var_init, (gen["qmax"]+gen["qmin"])/2) # non-standard start
push!(var_lb, gen["qmin"])
push!(var_ub, gen["qmax"])
var_lookup["qg_$(i)"] = var_idx
var_idx += 1
end
for (l, i, j) in ref[:arcs]
branch = ref[:branch][l]
push!(var_init, 0.0) #p
push!(var_lb, -branch["rate_a"])
push!(var_ub, branch["rate_a"])
var_lookup["p_$(l)_$(i)_$(j)"] = var_idx
var_idx += 1
push!(var_init, 0.0) #q
push!(var_lb, -branch["rate_a"])
push!(var_ub, branch["rate_a"])
var_lookup["q_$(l)_$(i)_$(j)"] = var_idx
var_idx += 1
end
@assert var_idx == length(var_init)+1
#total_callback_time = 0.0
function opf_objective(x)
#start = time()
cost = 0.0
for (i, gen) in ref[:gen]
pg = x[var_lookup["pg_$(i)"]]
cost += gen["cost"][1]*pg^2 + gen["cost"][2]*pg + gen["cost"][3]
end
#total_callback_time += time() - start
return cost
end
function opf_constraints(c, x)
#start = time()
va = Dict(i => x[var_lookup["va_$(i)"]] for (i, bus) in ref[:bus])
vm = Dict(i => x[var_lookup["vm_$(i)"]] for (i, bus) in ref[:bus])
pg = Dict(i => x[var_lookup["pg_$(i)"]] for (i, gen) in ref[:gen])
qg = Dict(i => x[var_lookup["qg_$(i)"]] for (i, gen) in ref[:gen])
p = Dict((l, i, j) => x[var_lookup["p_$(l)_$(i)_$(j)"]] for (l, i, j) in ref[:arcs])
q = Dict((l, i, j) => x[var_lookup["q_$(l)_$(i)_$(j)"]] for (l, i, j) in ref[:arcs])
vm_fr = Dict(l => vm[branch["f_bus"]] for (l, branch) in ref[:branch])
vm_to = Dict(l => vm[branch["t_bus"]] for (l, branch) in ref[:branch])
va_fr = Dict(l => va[branch["f_bus"]] for (l, branch) in ref[:branch])
va_to = Dict(l => va[branch["t_bus"]] for (l, branch) in ref[:branch])
va_con = [va[i] for (i, bus) in ref[:ref_buses]]
# @constraint(model,
# sum(p[a] for a in ref[:bus_arcs][i]) ==
# sum(pg[g] for g in ref[:bus_gens][i]) -
# sum(load["pd"] for load in bus_loads) -
# sum(shunt["gs"] for shunt in bus_shunts)*vm[i]^2
# )
power_balance_p_con = [sum(pg[j] for j in ref[:bus_gens][i]; init = 0.0) -
bus_pd[i] -
bus_gs[i]*vm[i]^2 -
sum(p[a] for a in ref[:bus_arcs][i])
for (i, bus) in ref[:bus]]
# @constraint(model,
# sum(q[a] for a in ref[:bus_arcs][i]) ==
# sum(qg[g] for g in ref[:bus_gens][i]) -
# sum(load["qd"] for load in bus_loads) +
# sum(shunt["bs"] for shunt in bus_shunts)*vm[i]^2
# )
power_balance_q_con = [sum(qg[j] for j in ref[:bus_gens][i]; init = 0.0) -
bus_qd[i] +
bus_bs[i]*vm[i]^2 -
sum(q[a] for a in ref[:bus_arcs][i])
for (i, bus) in ref[:bus]]
# @NLconstraint(model, p_fr == (g+g_fr)/ttm*vm_fr^2 + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-b*tr-g*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to)) )
power_flow_p_from_con = [(br_g[l]+br_g_fr[l])/br_ttm[l]*vm_fr[l]^2 +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*cos(va_fr[l]-va_to[l])) +
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*sin(va_fr[l]-va_to[l])) -
p[(l, i, j)]
for (l, i, j) in ref[:arcs_from]]
# @NLconstraint(model, p_to == (g+g_to)*vm_to^2 + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-b*tr+g*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr)) )
power_flow_p_to_con = [(br_g[l]+br_g_to[l])*vm_to[l]^2 +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*cos(va_to[l]-va_fr[l])) +
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*sin(va_to[l]-va_fr[l])) -
p[(l, i, j)]
for (l, i, j) in ref[:arcs_to]]
# @NLconstraint(model, q_fr == -(b+b_fr)/ttm*vm_fr^2 - (-b*tr-g*ti)/ttm*(vm_fr*vm_to*cos(va_fr-va_to)) + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*sin(va_fr-va_to)) )
power_flow_q_from_con = [-(br_b[l]+br_b_fr[l])/br_ttm[l]*vm_fr[l]^2 -
(-br_b[l]*br_tr[l]-br_g[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*cos(va_fr[l]-va_to[l])) +
(-br_g[l]*br_tr[l]+br_b[l]*br_ti[l])/br_ttm[l]*(vm_fr[l]*vm_to[l]*sin(va_fr[l]-va_to[l])) -
q[(l, i, j)]
for (l, i, j) in ref[:arcs_from]]
# @NLconstraint(model, q_to == -(b+b_to)*vm_to^2 - (-b*tr+g*ti)/ttm*(vm_to*vm_fr*cos(va_to-va_fr)) + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*sin(va_to-va_fr)) )
power_flow_q_to_con = [-(br_b[l]+br_b_to[l])*vm_to[l]^2 -
(-br_b[l]*br_tr[l]+br_g[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*cos(va_to[l]-va_fr[l])) +
(-br_g[l]*br_tr[l]-br_b[l]*br_ti[l])/br_ttm[l]*(vm_to[l]*vm_fr[l]*sin(va_to[l]-va_fr[l])) -
q[(l, i, j)]
for (l, i, j) in ref[:arcs_to]]
# @constraint(model, va_fr - va_to <= branch["angmax"])
# @constraint(model, va_fr - va_to >= branch["angmin"])
power_flow_vad_con = [va_fr[l] - va_to[l]
for (l, i, j) in ref[:arcs_from]]
# @constraint(model, p_fr^2 + q_fr^2 <= branch["rate_a"]^2)
power_flow_mva_from_con = [p[(l, i, j)]^2 + q[(l, i, j)]^2
for (l, i, j) in ref[:arcs_from]]
# @constraint(model, p_to^2 + q_to^2 <= branch["rate_a"]^2)
power_flow_mva_to_con = [p[(l, i, j)]^2 + q[(l, i, j)]^2
for (l, i, j) in ref[:arcs_to]]
c .= [
va_con...,
power_balance_p_con...,
power_balance_q_con...,
power_flow_p_from_con...,
power_flow_p_to_con...,
power_flow_q_from_con...,
power_flow_q_to_con...,
power_flow_vad_con...,
power_flow_mva_from_con...,
power_flow_mva_to_con...
]
#total_callback_time += time() - start
return c
end
con_lbs = Float64[]
con_ubs = Float64[]
#@constraint(model, va[i] == 0)
for (i, bus) in ref[:ref_buses]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_balance_p_con
for (i, bus) in ref[:bus]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
#push!(con_lbs, -Inf)
#push!(con_ubs, Inf)
end
#power_balance_q_con
for (i, bus) in ref[:bus]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
#push!(con_lbs, -Inf)
#push!(con_ubs, Inf)
end
#power_flow_p_from_con
for (l, i, j) in ref[:arcs_from]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_p_to_con
for (l, i, j) in ref[:arcs_to]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_q_from_con
for (l, i, j) in ref[:arcs_from]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_q_to_con
for (l, i, j) in ref[:arcs_to]
push!(con_lbs, 0.0)
push!(con_ubs, 0.0)
end
#power_flow_vad_con
for (l, i, j) in ref[:arcs_from]
branch = ref[:branch][l]
push!(con_lbs, branch["angmin"])
push!(con_ubs, branch["angmax"])
end
#power_flow_mva_from_con
for (l, i, j) in ref[:arcs_from]
branch = ref[:branch][l]
push!(con_lbs, -Inf)
push!(con_ubs, branch["rate_a"]^2)
end
#power_flow_mva_to_con
for (l, i, j) in ref[:arcs_to]
branch = ref[:branch][l]
push!(con_lbs, -Inf)
push!(con_ubs, branch["rate_a"]^2)
end
df = Optim.TwiceDifferentiable(opf_objective, var_init)
dfc = Optim.TwiceDifferentiableConstraints(
opf_constraints, var_lb, var_ub, con_lbs, con_ubs)
df, dfc, var_init, con_lbs, con_ubs
end
function solve_opf_optim(dataset)
model_build_time = @elapsed df, dfc, var_init, con_lbs,
con_ubs = build_opf_optim_prob(dataset)
options = Optim.Options(show_trace = PRINT_LEVEL != 0, time_limit = MAX_CPU_TIME)
solve_time_with_compilation = @elapsed res = Optim.optimize(
df, dfc, var_init, Optim.IPNewton(), options)
solve_time_without_compilation = @elapsed res = Optim.optimize(
df, dfc, var_init, Optim.IPNewton(), options)
sol = res.minimizer
cost = res.minimum
# NOTE: confirmed these constraint violations can be eliminated
# if a better starting point is used
sol_eval = dfc.c!(zeros(dfc.bounds.nc), sol)
vio_lb = [max(v, 0) for v in (con_lbs .- sol_eval)]
vio_ub = [max(v, 0) for v in (sol_eval .- con_ubs)]
const_vio = vio_lb .+ vio_ub
constraint_tol = 1e-6
feasible = (sum(const_vio) <= constraint_tol)
return (res,),
Dict(
"case" => file_name,
"variables" => length(var_init),
"constraints" => dfc.bounds.nc,
"feasible" => feasible,
"cost" => cost,
"time_build" => model_build_time,
"time_solve" => solve_time_without_compilation,
"time_solve_compilation" => solve_time_with_compilation
)
end
function test_optim_prob(dataset, test_u0)
df, dfc, var_init, con_lbs, con_ubs = build_opf_optim_prob(dataset)
obj = df.f(test_u0)
cons = dfc.c!(zeros(dfc.bounds.nc), test_u0)
obj, cons
endtest_optim_prob (generic function with 1 method)optim_test_res = test_optim_prob(dataset, test_u0)(16236.704322376236, [0.0, 2.5424107263916085e-14, -1.0835776720341528e-13,
-6.039613253960852e-14, 0.0, 0.0, 0.0, -1.7075230118734908e-13, -3.9968028
886505635e-14, 1.532107773982716e-13 … 5.709523923775402, 8.5822728368379
8, 18.147597689108025, 15.999999905294098, 3.0822827695389314, 2.6621176970
504, 5.759999990861611, 8.161419886019171, 17.65224849471505, 15.8096580240
1578])@assert optim_test_res[1] == test_obj@assert optim_test_res[2] == test_consCASADI
Implementation reference: https://github.com/lanl-ansi/PowerModelsAnnex.jl/blob/master/src/model/ac-opf.jl
CASADI Segfaults so removed for now.
import PowerModels
import PythonCall
import CondaPkg
CondaPkg.add("casadi")
function solve_opf_casadi(dataset)
(;data, ref) = dataset
time_model_start = time()
casadi = PythonCall.pyimport("casadi")
x, x0, lbx, ubx, cons, lbg, ubg = [], [], [], [], [], [], []
va, vm = Dict{Int,Any}(), Dict{Int,Any}()
for (k, _) in ref[:bus]
va[k] = casadi.SX.sym("va$k")
push!(x, va[k])
push!(x0, 0.0)
push!(lbx, -casadi.inf)
push!(ubx, casadi.inf)
vm[k] = casadi.SX.sym("vm$k")
push!(x, vm[k])
push!(x0, 1.0)
push!(lbx, ref[:bus][k]["vmin"])
push!(ubx, ref[:bus][k]["vmax"])
end
pg, qg = Dict{Int,Any}(), Dict{Int,Any}()
for (k, ) in ref[:gen]
pg[k] = casadi.SX.sym("pg$k")
push!(x, pg[k])
push!(x0, 0.0)
push!(lbx, ref[:gen][k]["pmin"])
push!(ubx, ref[:gen][k]["pmax"])
qg[k] = casadi.SX.sym("qg$k")
push!(x, qg[k])
push!(x0, 0.0)
push!(lbx, ref[:gen][k]["qmin"])
push!(ubx, ref[:gen][k]["qmax"])
end
p, q = Dict{NTuple{3,Int},Any}(), Dict{NTuple{3,Int},Any}()
for k in ref[:arcs]
a = ref[:branch][k[1]]["rate_a"]
p[k] = casadi.SX.sym("p$k")
push!(x, p[k])
push!(x0, 0.0)
push!(lbx, -a)
push!(ubx, a)
q[k] = casadi.SX.sym("q$k")
push!(x, q[k])
push!(x0, 0.0)
push!(lbx, -a)
push!(ubx, a)
end
f = sum(
cost["cost"][1] * pg[k]^2 +
cost["cost"][2] * pg[k] +
cost["cost"][3] for (k, cost) in ref[:gen]
)
for (k, _) in ref[:ref_buses]
push!(cons, va[k])
push!(lbg, 0)
push!(ubg, 0)
end
for (i, _) in ref[:bus]
bus_loads = [ref[:load][l] for l in ref[:bus_loads][i]]
bus_shunts = [ref[:shunt][s] for s in ref[:bus_shunts][i]]
push!(
cons,
sum(p[k] for k in ref[:bus_arcs][i]) -
sum(pg[g] for g in ref[:bus_gens][i]; init = 0) +
sum(load["pd"] for load in bus_loads; init = 0) +
sum(shunt["gs"] for shunt in bus_shunts; init = 0) * vm[i]^2
)
push!(lbg, 0)
push!(ubg, 0)
push!(
cons,
sum(q[k] for k in ref[:bus_arcs][i]) -
sum(qg[g] for g in ref[:bus_gens][i]; init = 0) +
sum(load["qd"] for load in bus_loads; init = 0) -
sum(shunt["bs"] for shunt in bus_shunts; init = 0) * vm[i]^2
)
push!(lbg, 0)
push!(ubg, 0)
end
for (i, branch) in ref[:branch]
f_idx = (i, branch["f_bus"], branch["t_bus"])
t_idx = (i, branch["t_bus"], branch["f_bus"])
p_fr = p[f_idx]
q_fr = q[f_idx]
p_to = p[t_idx]
q_to = q[t_idx]
vm_fr = vm[branch["f_bus"]]
vm_to = vm[branch["t_bus"]]
va_fr = va[branch["f_bus"]]
va_to = va[branch["t_bus"]]
g, b = PowerModels.calc_branch_y(branch)
tr, ti = PowerModels.calc_branch_t(branch)
ttm = tr^2 + ti^2
g_fr = branch["g_fr"]
b_fr = branch["b_fr"]
g_to = branch["g_to"]
b_to = branch["b_to"]
push!(
cons,
(g+g_fr)/ttm*vm_fr^2 + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*casadi.cos(va_fr-va_to)) + (-b*tr-g*ti)/ttm*(vm_fr*vm_to*casadi.sin(va_fr-va_to)) - p_fr
)
push!(
cons,
-(b+b_fr)/ttm*vm_fr^2 - (-b*tr-g*ti)/ttm*(vm_fr*vm_to*casadi.cos(va_fr-va_to)) + (-g*tr+b*ti)/ttm*(vm_fr*vm_to*casadi.sin(va_fr-va_to)) - q_fr
)
push!(
cons,
(g+g_to)*vm_to^2 + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*casadi.cos(va_to-va_fr)) + (-b*tr+g*ti)/ttm*(vm_to*vm_fr*casadi.sin(va_to-va_fr)) - p_to
)
push!(
cons,
-(b+b_to)*vm_to^2 - (-b*tr+g*ti)/ttm*(vm_to*vm_fr*casadi.cos(va_to-va_fr)) + (-g*tr-b*ti)/ttm*(vm_to*vm_fr*casadi.sin(va_to-va_fr)) - q_to
)
for i in 1:4
push!(lbg, 0)
push!(ubg, 0)
end
push!(cons, va_fr - va_to)
push!(lbg, branch["angmin"])
push!(ubg, branch["angmax"])
push!(cons, p_fr^2 + q_fr^2)
push!(lbg, -casadi.inf)
push!(ubg, branch["rate_a"]^2)
push!(cons, p_to^2 + q_to^2)
push!(lbg, -casadi.inf)
push!(ubg, branch["rate_a"]^2)
end
nlp = Dict("x" => casadi.vcat(x), "f" => f, "g" => casadi.vcat(cons))
options = PythonCall.pydict(Dict("error_on_fail" => true))
model = casadi.nlpsol("model", "ipopt", PythonCall.pydict(nlp), options)
model_variables = length(x)
model_constraints = length(lbg)
model_build_time = time() - time_model_start
time_solve_start = time()
solution = model(; lbx = lbx, ubx = ubx, lbg = lbg, ubg = ubg, x0 = x0)
cost = PythonCall.pyconvert(Float64, (PythonCall.pyfloat(solution["f"])))
feasible = true # error if not feasible
solve_time = time() - time_solve_start
total_time = time() - time_data_start
println("")
println("\033[1mSummary\033[0m")
println(" case........: $(file_name)")
println(" variables...: $(model_variables)")
println(" constraints.: $(model_constraints)")
println(" feasible....: $(feasible)")
println(" cost........: $(round(Int, cost))")
println(" total time..: $(total_time)")
println(" data time.: $(data_load_time)")
println(" build time: $(model_build_time)")
println(" solve time: $(solve_time)")
# println(" callbacks: $(total_callback_time)")
println("")
return Dict(
"case" => file_name,
"variables" => model_variables,
"constraints" => model_constraints,
"feasible" => feasible,
"cost" => cost,
"time_total" => total_time,
"time_data" => data_load_time,
"time_build" => model_build_time,
"time_solve" => solve_time,
#"time_callbacks" => TBD,
)
end
solve_opf_casadi("../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m")Test the Benchmarking
file_name = "../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m"
dataset = load_and_setup_data(file_name);model, res = solve_opf_optimization(dataset);
res***************************************************************************
***
This program contains Ipopt, a library for large-scale nonlinear optimizati
on.
Ipopt is released as open source code under the Eclipse Public License (EP
L).
For more information visit https://github.com/coin-or/Ipopt
***************************************************************************
***
Dict{String, Any} with 8 entries:
"cost" => 17551.9
"variables" => 44
"constraints" => 53
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.0953678
"time_solve_compilation" => 18.634
"time_solve" => 0.724718
"feasible" => truemodel, res = solve_opf_jump(dataset);
resDict{String, Any} with 8 entries:
"cost" => 17551.9
"variables" => 44
"constraints" => 53
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.00365001
"time_solve_compilation" => 7.42519
"time_solve" => 0.0133982
"feasible" => truemodel, res = solve_opf_nlpmodels(dataset);
resDict{String, Any} with 8 entries:
"cost" => 17551.9
"variables" => 44
"constraints" => 53
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.0116992
"time_solve_compilation" => 4.34116
"time_solve" => 0.0477297
"feasible" => true# Nonconvex disabled - incompatible with Symbolics v7
# model, res = solve_opf_nonconvex(dataset);
# resmodel, res = solve_opf_optim(dataset);
resDict{String, Any} with 8 entries:
"cost" => 90.6969
"variables" => 44
"constraints" => 53
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.00155903
"time_solve_compilation" => 18.1014
"time_solve" => 12.8387
"feasible" => falsefile_name = "../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case3_lmbd.m"
dataset = load_and_setup_data(file_name);model, res = solve_opf_optimization(dataset);
resDict{String, Any} with 8 entries:
"cost" => 5812.64
"variables" => 24
"constraints" => 28
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.000316998
"time_solve_compilation" => 0.216039
"time_solve" => 0.325786
"feasible" => truemodel, res = solve_opf_jump(dataset);
resDict{String, Any} with 8 entries:
"cost" => 5812.64
"variables" => 24
"constraints" => 28
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.00258486
"time_solve_compilation" => 0.0105703
"time_solve" => 0.0095622
"feasible" => truemodel, res = solve_opf_nlpmodels(dataset);
resDict{String, Any} with 8 entries:
"cost" => 5812.64
"variables" => 24
"constraints" => 28
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.00521975
"time_solve_compilation" => 0.0264786
"time_solve" => 0.0263811
"feasible" => true# Nonconvex disabled - incompatible with Symbolics v7
# model, res = solve_opf_nonconvex(dataset);
# resmodel, res = solve_opf_optim(dataset);
resDict{String, Any} with 8 entries:
"cost" => 1.93871e5
"variables" => 24
"constraints" => 28
"case" => "../../benchmarks/OptimizationFrameworks/opf_
data…
"time_build" => 0.335146
"time_solve_compilation" => 0.500173
"time_solve" => 0.617059
"feasible" => falseusing DataFrames, PrettyTables
function multidata_multisolver_benchmark(dataset_files; sizelimit = SIZE_LIMIT)
cases = String[]
vars = Int[]
cons = Int[]
optimization_time = Float64[]
mtk_time = Float64[]
jump_time = Float64[]
nlpmodels_time = Float64[]
optim_time = Float64[]
optimization_time_modelbuild = Float64[]
mtk_time_modelbuild = Float64[]
jump_time_modelbuild = Float64[]
nlpmodels_time_modelbuild = Float64[]
optim_time_modelbuild = Float64[]
optimization_time_compilation = Float64[]
mtk_time_compilation = Float64[]
jump_time_compilation = Float64[]
nlpmodels_time_compilation = Float64[]
optim_time_compilation = Float64[]
optimization_cost = Float64[]
mtk_cost = Float64[]
jump_cost = Float64[]
nlpmodels_cost = Float64[]
optim_cost = Float64[]
for file in dataset_files
@show file
dataset = load_and_setup_data(file)
model_variables = length(dataset.var_init)
@info "Number of Variables: $(model_variables)"
if model_variables > sizelimit
@info "Variable size over global limit. Skipping for now"
continue
end
@info "Running Optimization.jl"
model, res = solve_opf_optimization(dataset)
@info "Number of Constraints: $(res["constraints"])"
push!(cases, split(file, "/")[end])
push!(vars, res["variables"])
push!(cons, res["constraints"])
push!(optimization_time, res["time_solve"])
push!(optimization_time_modelbuild, res["time_build"])
push!(optimization_time_compilation, res["time_solve_compilation"])
push!(optimization_cost, res["cost"])
@info "Running ModelingToolkit.jl"
model, res = solve_opf_mtk(dataset)
push!(mtk_time, res["time_solve"])
push!(mtk_time_modelbuild, res["time_build"])
push!(mtk_time_compilation, res["time_solve_compilation"])
push!(mtk_cost, res["cost"])
@info "Running JuMP.jl"
model, res = solve_opf_jump(dataset)
push!(jump_time, res["time_solve"])
push!(jump_time_modelbuild, res["time_build"])
push!(jump_time_compilation, res["time_solve_compilation"])
push!(jump_cost, res["cost"])
@info "Running NLPModels.jl"
model, res = solve_opf_nlpmodels(dataset)
push!(nlpmodels_time, res["time_solve"])
push!(nlpmodels_time_modelbuild, res["time_build"])
push!(nlpmodels_time_compilation, res["time_solve_compilation"])
push!(nlpmodels_cost, res["cost"])
#=
@info "Running Nonconvex.jl"
model, res = solve_opf_nonconvex(dataset)
push!(nonconvex_time, res["time_solve"])
push!(nonconvex_time_modelbuild, res["time_build"])
push!(nonconvex_time_compilation, res["time_solve_compilation"])
push!(nonconvex_cost, res["cost"])
=#
if model_variables > 400
@info "Variable size over Optim.jl limit. Skipping for now"
push!(optim_time, NaN)
push!(optim_time_modelbuild, NaN)
push!(optim_time_compilation, NaN)
push!(optim_cost, NaN)
else
@info "Running Optim.jl"
model, res = solve_opf_optim(dataset)
push!(optim_time, res["time_solve"])
push!(optim_time_modelbuild, res["time_build"])
push!(optim_time_compilation, res["time_solve_compilation"])
push!(optim_cost, res["cost"])
end
end
DataFrame(:case => cases, :vars => vars, :cons => cons,
:optimization => optimization_time, :optimization_modelbuild => optimization_time_modelbuild,
:optimization_wcompilation => optimization_time_compilation,
:optimization_cost => optimization_cost,
:mtk => mtk_time, :mtk_time_modelbuild => mtk_time_modelbuild,
:mtk_time_wcompilation => mtk_time_compilation, :mtk_cost => mtk_cost,
:jump => jump_time, :jump_modelbuild => jump_time_modelbuild,
:jump_wcompilation => jump_time_compilation, :jump_cost => jump_cost,
:nlpmodels => nlpmodels_time, :nlpmodels_modelbuild => nlpmodels_time_modelbuild,
:nlpmodels_wcompilation => nlpmodels_time_compilation,
:nlpmodels_cost => nlpmodels_cost,
#:nonconvex => nonconvex_time, :nonconvex_modelbuild => nonconvex_time_modelbuild, :nonconvex_wcompilation => nonconvex_time_compilation, :nonconvex_cost => nonconvex_cost,
:optim => optim_time, :optim_modelbuild => optim_time_modelbuild,
:optim_wcompilation => optim_time_compilation, :optim_cost => optim_cost)
end
test_datasets = [
"../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case3_lmbd.m",
"../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m"
]2-element Vector{String}:
"../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case3_lmbd.m"
"../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m"timing_data = multidata_multisolver_benchmark(test_datasets)file = "../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case3_lmbd.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 78
Number of nonzeros in inequality constraint Jacobian.: 24
Number of nonzeros in Lagrangian Hessian.............: 122
Total number of variables............................: 23
variables with only lower bounds: 0
variables with lower and upper bounds: 20
variables with only upper bounds: 0
Total number of equality constraints.................: 19
Total number of inequality constraints...............: 12
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 12
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.3949934e+00 1.09e+00 1.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.1048421e+03 4.62e-01 1.04e+02 -1.0 1.81e+00 - 5.77e-03 5.76e-01h 1
2 4.4503068e+03 1.36e-01 3.09e+01 -1.0 8.29e-01 - 8.15e-01 7.05e-01h 1
3 4.6140270e+03 1.18e-01 2.67e+01 -1.0 2.99e-01 - 4.13e-01 1.37e-01h 1
4 4.9343698e+03 8.25e-02 3.85e+01 -1.0 4.36e-01 - 4.94e-01 2.98e-01h 1
5 5.4019378e+03 3.46e-02 2.51e+01 -1.0 3.62e-01 - 9.90e-01 5.81e-01h 1
6 5.4116196e+03 3.37e-02 1.26e+02 -1.0 1.45e-01 - 1.84e-01 2.70e-02h 1
7 5.6094774e+03 1.60e-02 5.13e+01 -1.0 2.53e-01 - 2.76e-01 5.25e-01h 1
8 5.7031738e+03 8.53e-03 8.21e+01 -1.0 1.43e-01 - 7.00e-01 4.67e-01h 1
9 5.8145457e+03 6.10e-04 2.19e+00 -1.0 5.77e-02 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 5.8146316e+03 2.55e-05 7.83e-03 -1.0 1.23e-02 - 1.00e+00 1.00e+00h 1
11 5.8127612e+03 1.60e-05 1.36e-02 -2.5 8.27e-03 - 1.00e+00 1.00e+00f 1
12 5.8126464e+03 2.60e-07 1.15e-04 -3.8 1.05e-03 - 1.00e+00 1.00e+00f 1
13 5.8126430e+03 1.32e-10 8.31e-08 -5.7 2.50e-05 - 1.00e+00 1.00e+00h 1
14 5.8126429e+03 7.77e-15 6.25e-12 -8.6 1.78e-07 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 14
(scaled) (unscaled)
Objective...............: 1.1625285870072362e+03 5.8126429350361805e+03
Dual infeasibility......: 6.2541516535614795e-12 3.1270758267807398e-11
Constraint violation....: 7.7715611723760958e-15 7.7715611723760958e-15
Variable bound violation: 1.0911841874516881e-08 1.0911841874516881e-08
Complementarity.........: 2.5102170848799279e-09 1.2551085424399639e-08
Overall NLP error.......: 2.5102170848799279e-09 1.2551085424399639e-08
Number of objective function evaluations = 15
Number of objective gradient evaluations = 15
Number of equality constraint evaluations = 15
Number of inequality constraint evaluations = 15
Number of equality constraint Jacobian evaluations = 15
Number of inequality constraint Jacobian evaluations = 15
Number of Lagrangian Hessian evaluations = 14
Total seconds in IPOPT = 0.932
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 78
Number of nonzeros in inequality constraint Jacobian.: 24
Number of nonzeros in Lagrangian Hessian.............: 122
Total number of variables............................: 23
variables with only lower bounds: 0
variables with lower and upper bounds: 20
variables with only upper bounds: 0
Total number of equality constraints.................: 19
Total number of inequality constraints...............: 12
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 12
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.3949934e+00 1.09e+00 1.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.1048421e+03 4.62e-01 1.04e+02 -1.0 1.81e+00 - 5.77e-03 5.76e-01h 1
2 4.4503068e+03 1.36e-01 3.09e+01 -1.0 8.29e-01 - 8.15e-01 7.05e-01h 1
3 4.6140270e+03 1.18e-01 2.67e+01 -1.0 2.99e-01 - 4.13e-01 1.37e-01h 1
4 4.9343698e+03 8.25e-02 3.85e+01 -1.0 4.36e-01 - 4.94e-01 2.98e-01h 1
5 5.4019378e+03 3.46e-02 2.51e+01 -1.0 3.62e-01 - 9.90e-01 5.81e-01h 1
6 5.4116196e+03 3.37e-02 1.26e+02 -1.0 1.45e-01 - 1.84e-01 2.70e-02h 1
7 5.6094774e+03 1.60e-02 5.13e+01 -1.0 2.53e-01 - 2.76e-01 5.25e-01h 1
8 5.7031738e+03 8.53e-03 8.21e+01 -1.0 1.43e-01 - 7.00e-01 4.67e-01h 1
9 5.8145457e+03 6.10e-04 2.19e+00 -1.0 5.77e-02 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 5.8146316e+03 2.55e-05 7.83e-03 -1.0 1.23e-02 - 1.00e+00 1.00e+00h 1
11 5.8127612e+03 1.60e-05 1.36e-02 -2.5 8.27e-03 - 1.00e+00 1.00e+00f 1
12 5.8126464e+03 2.60e-07 1.15e-04 -3.8 1.05e-03 - 1.00e+00 1.00e+00f 1
13 5.8126430e+03 1.32e-10 8.31e-08 -5.7 2.50e-05 - 1.00e+00 1.00e+00h 1
14 5.8126429e+03 7.77e-15 6.25e-12 -8.6 1.78e-07 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 14
(scaled) (unscaled)
Objective...............: 1.1625285870072362e+03 5.8126429350361805e+03
Dual infeasibility......: 6.2541516535614795e-12 3.1270758267807398e-11
Constraint violation....: 7.7715611723760958e-15 7.7715611723760958e-15
Variable bound violation: 1.0911841874516881e-08 1.0911841874516881e-08
Complementarity.........: 2.5102170848799279e-09 1.2551085424399639e-08
Overall NLP error.......: 2.5102170848799279e-09 1.2551085424399639e-08
Number of objective function evaluations = 15
Number of objective gradient evaluations = 15
Number of equality constraint evaluations = 15
Number of inequality constraint evaluations = 15
Number of equality constraint Jacobian evaluations = 15
Number of inequality constraint Jacobian evaluations = 15
Number of Lagrangian Hessian evaluations = 14
Total seconds in IPOPT = 0.005
EXIT: Optimal Solution Found.
file = "../../benchmarks/OptimizationFrameworks/opf_data/pglib_opf_case5_pjm.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 155
Number of nonzeros in inequality constraint Jacobian.: 48
Number of nonzeros in Lagrangian Hessian.............: 240
Total number of variables............................: 44
variables with only lower bounds: 0
variables with lower and upper bounds: 39
variables with only upper bounds: 0
Total number of equality constraints.................: 35
Total number of inequality constraints...............: 24
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 24
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0059989e+02 3.99e+00 2.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 8.3066305e+03 2.47e+00 1.01e+02 -1.0 2.78e+00 - 4.11e-03 3.82e-01h 1
2 6.7181372e+03 2.36e+00 9.62e+01 -1.0 1.60e+01 - 7.37e-02 4.44e-02f 1
3 6.6689587e+03 2.30e+00 9.34e+01 -1.0 1.30e+01 - 4.94e-01 2.40e-02f 1
4 6.5741805e+03 2.04e+00 8.25e+01 -1.0 1.29e+01 - 3.67e-01 1.12e-01f 2
5 6.8264259e+03 1.80e+00 7.10e+01 -1.0 1.23e+01 - 8.72e-01 1.20e-01h 2
6 8.8540136e+03 1.08e+00 4.20e+01 -1.0 9.14e+00 - 5.92e-01 4.00e-01h 1
7 1.0572806e+04 8.62e-01 3.58e+01 -1.0 2.94e+00 - 4.93e-01 2.00e-01h 1
8 1.7308577e+04 3.63e-02 1.46e+01 -1.0 2.41e+00 - 7.65e-01 9.58e-01h 1
9 1.7572869e+04 1.33e-02 1.10e+00 -1.0 2.11e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7590631e+04 1.68e-03 1.61e-01 -1.0 5.04e-01 - 1.00e+00 1.00e+00h 1
11 1.7558724e+04 5.24e-03 5.03e-01 -2.5 6.03e-01 - 8.35e-01 9.36e-01f 1
12 1.7553111e+04 3.34e-03 4.12e+00 -2.5 2.84e-01 - 1.00e+00 8.20e-01h 1
13 1.7552956e+04 3.24e-05 1.26e-02 -2.5 6.35e-02 - 1.00e+00 1.00e+00h 1
14 1.7551990e+04 1.35e-05 1.09e+00 -3.8 2.53e-02 - 1.00e+00 9.25e-01h 1
15 1.7551938e+04 4.46e-08 1.22e-02 -3.8 7.00e-03 - 1.00e+00 1.00e+00f 1
16 1.7551940e+04 2.35e-10 2.06e-04 -3.8 3.83e-04 - 1.00e+00 1.00e+00h 1
17 1.7551893e+04 1.75e-07 2.10e-01 -5.7 2.49e-03 - 1.00e+00 9.68e-01f 1
18 1.7551891e+04 6.80e-11 3.09e-05 -5.7 2.38e-04 - 1.00e+00 1.00e+00f 1
19 1.7551891e+04 3.06e-14 6.47e-10 -5.7 5.17e-07 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.7551891e+04 6.26e-12 3.03e-07 -8.6 3.52e-05 - 1.00e+00 1.00e+00f 1
21 1.7551891e+04 2.92e-14 3.65e-12 -8.6 3.33e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 21
(scaled) (unscaled)
Objective...............: 4.3879727248486864e+02 1.7551890899394744e+04
Dual infeasibility......: 3.6484809026628434e-12 1.4593923610651373e-10
Constraint violation....: 1.8856205485917602e-14 2.9171109972025988e-14
Variable bound violation: 2.9463905093507492e-08 2.9463905093507492e-08
Complementarity.........: 2.5059076126554735e-09 1.0023630450621893e-07
Overall NLP error.......: 2.5059076126554735e-09 1.0023630450621893e-07
Number of objective function evaluations = 28
Number of objective gradient evaluations = 22
Number of equality constraint evaluations = 28
Number of inequality constraint evaluations = 28
Number of equality constraint Jacobian evaluations = 22
Number of inequality constraint Jacobian evaluations = 22
Number of Lagrangian Hessian evaluations = 21
Total seconds in IPOPT = 2.077
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 155
Number of nonzeros in inequality constraint Jacobian.: 48
Number of nonzeros in Lagrangian Hessian.............: 240
Total number of variables............................: 44
variables with only lower bounds: 0
variables with lower and upper bounds: 39
variables with only upper bounds: 0
Total number of equality constraints.................: 35
Total number of inequality constraints...............: 24
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 24
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0059989e+02 3.99e+00 2.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 8.3066305e+03 2.47e+00 1.01e+02 -1.0 2.78e+00 - 4.11e-03 3.82e-01h 1
2 6.7181372e+03 2.36e+00 9.62e+01 -1.0 1.60e+01 - 7.37e-02 4.44e-02f 1
3 6.6689587e+03 2.30e+00 9.34e+01 -1.0 1.30e+01 - 4.94e-01 2.40e-02f 1
4 6.5741805e+03 2.04e+00 8.25e+01 -1.0 1.29e+01 - 3.67e-01 1.12e-01f 2
5 6.8264259e+03 1.80e+00 7.10e+01 -1.0 1.23e+01 - 8.72e-01 1.20e-01h 2
6 8.8540136e+03 1.08e+00 4.20e+01 -1.0 9.14e+00 - 5.92e-01 4.00e-01h 1
7 1.0572806e+04 8.62e-01 3.58e+01 -1.0 2.94e+00 - 4.93e-01 2.00e-01h 1
8 1.7308577e+04 3.63e-02 1.46e+01 -1.0 2.41e+00 - 7.65e-01 9.58e-01h 1
9 1.7572869e+04 1.33e-02 1.10e+00 -1.0 2.11e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7590631e+04 1.68e-03 1.61e-01 -1.0 5.04e-01 - 1.00e+00 1.00e+00h 1
11 1.7558724e+04 5.24e-03 5.03e-01 -2.5 6.03e-01 - 8.35e-01 9.36e-01f 1
12 1.7553111e+04 3.34e-03 4.12e+00 -2.5 2.84e-01 - 1.00e+00 8.20e-01h 1
13 1.7552956e+04 3.24e-05 1.26e-02 -2.5 6.35e-02 - 1.00e+00 1.00e+00h 1
14 1.7551990e+04 1.35e-05 1.09e+00 -3.8 2.53e-02 - 1.00e+00 9.25e-01h 1
15 1.7551938e+04 4.46e-08 1.22e-02 -3.8 7.00e-03 - 1.00e+00 1.00e+00f 1
16 1.7551940e+04 2.35e-10 2.06e-04 -3.8 3.83e-04 - 1.00e+00 1.00e+00h 1
17 1.7551893e+04 1.75e-07 2.10e-01 -5.7 2.49e-03 - 1.00e+00 9.68e-01f 1
18 1.7551891e+04 6.80e-11 3.09e-05 -5.7 2.38e-04 - 1.00e+00 1.00e+00f 1
19 1.7551891e+04 3.06e-14 6.47e-10 -5.7 5.17e-07 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.7551891e+04 6.26e-12 3.03e-07 -8.6 3.52e-05 - 1.00e+00 1.00e+00f 1
21 1.7551891e+04 2.92e-14 3.65e-12 -8.6 3.33e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 21
(scaled) (unscaled)
Objective...............: 4.3879727248486864e+02 1.7551890899394744e+04
Dual infeasibility......: 3.6484809026628434e-12 1.4593923610651373e-10
Constraint violation....: 1.8856205485917602e-14 2.9171109972025988e-14
Variable bound violation: 2.9463905093507492e-08 2.9463905093507492e-08
Complementarity.........: 2.5059076126554735e-09 1.0023630450621893e-07
Overall NLP error.......: 2.5059076126554735e-09 1.0023630450621893e-07
Number of objective function evaluations = 28
Number of objective gradient evaluations = 22
Number of equality constraint evaluations = 28
Number of inequality constraint evaluations = 28
Number of equality constraint Jacobian evaluations = 22
Number of inequality constraint Jacobian evaluations = 22
Number of Lagrangian Hessian evaluations = 21
Total seconds in IPOPT = 0.010
EXIT: Optimal Solution Found.
2×23 DataFrame
Row │ case vars cons optimization optimization_modelb ⋯
│ String Int64 Int64 Float64 Float64 ⋯
─────┼──────────────────────────────────────────────────────────────────────────
1 │ pglib_opf_case3_lmbd.m 24 28 0.282001 0.00022 ⋯
2 │ pglib_opf_case5_pjm.m 44 53 0.830424 0.00019
19 columns omittedio = IOBuffer()
println(io, "```@raw html")
pretty_table(io, timing_data; backend = :html)
# show(io, "text/html", pretty_table(timing_data; backend = :html))
println(io, "```")
Text(String(take!(io)))| case | vars | cons | optimization | optimization_modelbuild | optimization_wcompilation | optimization_cost | mtk | mtk_time_modelbuild | mtk_time_wcompilation | mtk_cost | jump | jump_modelbuild | jump_wcompilation | jump_cost | nlpmodels | nlpmodels_modelbuild | nlpmodels_wcompilation | nlpmodels_cost | optim | optim_modelbuild | optim_wcompilation | optim_cost |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| String | Int64 | Int64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 |
| pglib_opf_case3_lmbd.m | 24 | 28 | 0.282001 | 0.000227748 | 0.215498 | 5812.64 | 0.0389134 | 0.263891 | 4.07625 | 5812.64 | 0.00815264 | 0.00285694 | 0.00924784 | 5812.64 | 0.0236723 | 0.00491654 | 0.0236552 | 5812.64 | 0.605673 | 0.000849274 | 0.549066 | 1.93871e5 |
| pglib_opf_case5_pjm.m | 44 | 53 | 0.830424 | 0.000195829 | 0.799336 | 17551.9 | 0.0270036 | 0.137459 | 3.41895 | 17551.9 | 0.0127802 | 0.00311729 | 0.0138209 | 17551.9 | 0.0460711 | 0.00692568 | 0.0510126 | 17551.9 | 12.6657 | 0.000983783 | 12.5123 | 90.6969 |
Run the Full Benchmark
using LibGit2
tmpdir = Base.Filesystem.mktempdir()
LibGit2.clone("https://github.com/power-grid-lib/pglib-opf", tmpdir)
benchmarkfiles = readdir(tmpdir)
benchmarkfiles = benchmarkfiles[endswith(".m").(benchmarkfiles)]
benchmark_datasets = joinpath.((tmpdir,), benchmarkfiles)66-element Vector{String}:
"/tmp/jl_3GWXAw/pglib_opf_case10000_goc.m"
"/tmp/jl_3GWXAw/pglib_opf_case10192_epigrids.m"
"/tmp/jl_3GWXAw/pglib_opf_case10480_goc.m"
"/tmp/jl_3GWXAw/pglib_opf_case118_ieee.m"
"/tmp/jl_3GWXAw/pglib_opf_case1354_pegase.m"
"/tmp/jl_3GWXAw/pglib_opf_case13659_pegase.m"
"/tmp/jl_3GWXAw/pglib_opf_case14_ieee.m"
"/tmp/jl_3GWXAw/pglib_opf_case162_ieee_dtc.m"
"/tmp/jl_3GWXAw/pglib_opf_case179_goc.m"
"/tmp/jl_3GWXAw/pglib_opf_case1803_snem.m"
⋮
"/tmp/jl_3GWXAw/pglib_opf_case6515_rte.m"
"/tmp/jl_3GWXAw/pglib_opf_case7336_epigrids.m"
"/tmp/jl_3GWXAw/pglib_opf_case73_ieee_rts.m"
"/tmp/jl_3GWXAw/pglib_opf_case78484_epigrids.m"
"/tmp/jl_3GWXAw/pglib_opf_case793_goc.m"
"/tmp/jl_3GWXAw/pglib_opf_case8387_pegase.m"
"/tmp/jl_3GWXAw/pglib_opf_case89_pegase.m"
"/tmp/jl_3GWXAw/pglib_opf_case9241_pegase.m"
"/tmp/jl_3GWXAw/pglib_opf_case9591_goc.m"timing_data = multidata_multisolver_benchmark(benchmark_datasets)file = "/tmp/jl_3GWXAw/pglib_opf_case10000_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case10192_epigrids.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case10480_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case118_ieee.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case1354_pegase.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case13659_pegase.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case14_ieee.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 489
Number of nonzeros in inequality constraint Jacobian.: 160
Number of nonzeros in Lagrangian Hessian.............: 791
Total number of variables............................: 115
variables with only lower bounds: 0
variables with lower and upper bounds: 101
variables with only upper bounds: 0
Total number of equality constraints.................: 109
Total number of inequality constraints...............: 80
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 80
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.1649922e+01 9.42e-01 1.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.4464214e+03 2.50e-01 1.33e+02 -1.0 1.81e+00 - 5.61e-03 7.34e-01H 1
2 2.3967630e+03 4.55e-02 8.94e+01 -1.0 1.75e+00 - 1.74e-02 9.97e-01f 1
3 2.1633361e+03 4.42e-02 8.75e+01 -1.0 2.61e+01 - 2.18e-01 1.95e-02f 1
4 2.1823081e+03 8.47e-04 1.41e+01 -1.0 8.82e-01 - 7.57e-01 1.00e+00h 1
5 2.1890842e+03 3.76e-03 7.25e-01 -1.0 6.67e-02 - 1.00e+00 1.00e+00f 1
6 2.1849633e+03 4.30e-04 5.24e-01 -1.7 1.38e-02 - 9.64e-01 1.00e+00h 1
7 2.1794160e+03 2.31e-03 2.33e-02 -1.7 4.13e-02 - 1.00e+00 1.00e+00f 1
8 2.1786673e+03 6.12e-04 2.89e-01 -3.8 7.90e-03 - 8.46e-01 7.40e-01h 1
9 2.1780966e+03 1.45e-04 3.05e-01 -3.8 4.14e-02 - 4.43e-01 9.69e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 2.1780936e+03 7.00e-06 6.66e-04 -3.8 1.35e-02 - 1.00e+00 1.00e+00h 1
11 2.1780803e+03 1.65e-06 9.68e-04 -5.7 4.57e-03 - 9.56e-01 9.45e-01h 1
12 2.1780805e+03 1.82e-07 1.04e-05 -5.7 1.67e-03 - 1.00e+00 1.00e+00h 1
13 2.1780804e+03 2.19e-09 2.91e-06 -8.6 1.68e-04 - 1.00e+00 9.99e-01h 1
14 2.1780804e+03 1.10e-09 1.44e-05 -8.6 2.01e-06 - 1.00e+00 5.00e-01f 2
15 2.1780804e+03 6.57e-14 3.79e-12 -8.6 1.01e-06 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 9.3602396804143893e+01 2.1780804108196457e+03
Dual infeasibility......: 3.7925218521195347e-12 8.8250064482764413e-11
Constraint violation....: 6.5725203057809267e-14 6.5725203057809267e-14
Variable bound violation: 1.0340993394919451e-08 1.0340993394919451e-08
Complementarity.........: 2.5059040485519255e-09 5.8311119222354745e-08
Overall NLP error.......: 2.5059040485519255e-09 5.8311119222354745e-08
Number of objective function evaluations = 18
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 18
Number of inequality constraint evaluations = 18
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total seconds in IPOPT = 14.033
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 489
Number of nonzeros in inequality constraint Jacobian.: 160
Number of nonzeros in Lagrangian Hessian.............: 791
Total number of variables............................: 115
variables with only lower bounds: 0
variables with lower and upper bounds: 101
variables with only upper bounds: 0
Total number of equality constraints.................: 109
Total number of inequality constraints...............: 80
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 80
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.1649922e+01 9.42e-01 1.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.4464214e+03 2.50e-01 1.33e+02 -1.0 1.81e+00 - 5.61e-03 7.34e-01H 1
2 2.3967630e+03 4.55e-02 8.94e+01 -1.0 1.75e+00 - 1.74e-02 9.97e-01f 1
3 2.1633361e+03 4.42e-02 8.75e+01 -1.0 2.61e+01 - 2.18e-01 1.95e-02f 1
4 2.1823081e+03 8.47e-04 1.41e+01 -1.0 8.82e-01 - 7.57e-01 1.00e+00h 1
5 2.1890842e+03 3.76e-03 7.25e-01 -1.0 6.67e-02 - 1.00e+00 1.00e+00f 1
6 2.1849633e+03 4.30e-04 5.24e-01 -1.7 1.38e-02 - 9.64e-01 1.00e+00h 1
7 2.1794160e+03 2.31e-03 2.33e-02 -1.7 4.13e-02 - 1.00e+00 1.00e+00f 1
8 2.1786673e+03 6.12e-04 2.89e-01 -3.8 7.90e-03 - 8.46e-01 7.40e-01h 1
9 2.1780966e+03 1.45e-04 3.05e-01 -3.8 4.14e-02 - 4.43e-01 9.69e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 2.1780936e+03 7.00e-06 6.66e-04 -3.8 1.35e-02 - 1.00e+00 1.00e+00h 1
11 2.1780803e+03 1.65e-06 9.68e-04 -5.7 4.57e-03 - 9.56e-01 9.45e-01h 1
12 2.1780805e+03 1.82e-07 1.04e-05 -5.7 1.67e-03 - 1.00e+00 1.00e+00h 1
13 2.1780804e+03 2.19e-09 2.91e-06 -8.6 1.68e-04 - 1.00e+00 9.99e-01h 1
14 2.1780804e+03 1.10e-09 1.44e-05 -8.6 2.01e-06 - 1.00e+00 5.00e-01f 2
15 2.1780804e+03 6.57e-14 3.79e-12 -8.6 1.01e-06 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 9.3602396804143893e+01 2.1780804108196457e+03
Dual infeasibility......: 3.7925218521195347e-12 8.8250064482764413e-11
Constraint violation....: 6.5725203057809267e-14 6.5725203057809267e-14
Variable bound violation: 1.0340993394919451e-08 1.0340993394919451e-08
Complementarity.........: 2.5059040485519255e-09 5.8311119222354745e-08
Overall NLP error.......: 2.5059040485519255e-09 5.8311119222354745e-08
Number of objective function evaluations = 18
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 18
Number of inequality constraint evaluations = 18
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total seconds in IPOPT = 0.024
EXIT: Optimal Solution Found.
file = "/tmp/jl_3GWXAw/pglib_opf_case162_ieee_dtc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case179_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case1803_snem.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case1888_rte.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case19402_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case1951_rte.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case197_snem.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2000_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case200_activ.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case20758_epigrids.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2312_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2383wp_k.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case240_pserc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case24464_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case24_ieee_rts.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 979
Number of nonzeros in inequality constraint Jacobian.: 304
Number of nonzeros in Lagrangian Hessian.............: 1543
Total number of variables............................: 265
variables with only lower bounds: 0
variables with lower and upper bounds: 241
variables with only upper bounds: 0
Total number of equality constraints.................: 201
Total number of inequality constraints...............: 152
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 152
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 4.0097983e+04 2.52e+00 4.56e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 5.6173148e+04 1.56e+00 6.27e+01 -1.0 2.38e+00 - 1.00e-02 3.79e-01h 1
2 5.3163376e+04 1.35e+00 5.43e+01 -1.0 6.42e+00 - 1.34e-02 1.39e-01f 1
3 5.2196944e+04 1.33e+00 5.02e+01 -1.0 8.86e+00 - 3.43e-01 1.34e-02f 1
4 5.3182043e+04 1.06e+00 3.73e+01 -1.0 7.58e+00 - 7.33e-01 2.03e-01h 1
5 5.5137462e+04 3.87e-01 1.31e+01 -1.0 9.48e+00 - 9.74e-01 6.34e-01H 1
6 5.4942906e+04 3.18e-01 1.19e+01 -1.0 1.03e+01 - 4.67e-01 1.79e-01f 1
7 5.5363333e+04 2.73e-01 1.12e+01 -1.0 7.18e+00 - 1.00e+00 1.42e-01h 1
8 5.5935463e+04 2.33e-01 6.62e+00 -1.0 3.08e+00 - 2.59e-01 1.44e-01h 1
9 6.2388698e+04 6.41e-02 5.12e+00 -1.0 1.40e+00 - 6.92e-01 8.82e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 6.3791569e+04 2.81e-03 8.67e-01 -1.0 1.80e+00 - 7.56e-01 1.00e+00h 1
11 6.3501031e+04 8.45e-04 6.21e-01 -1.7 1.69e-01 - 8.51e-01 1.00e+00f 1
12 6.3454928e+04 1.01e-03 1.25e-02 -1.7 1.02e-01 - 1.00e+00 1.00e+00h 1
13 6.3378078e+04 8.53e-04 1.25e-01 -3.8 1.49e-01 - 8.03e-01 7.30e-01f 1
14 6.3364846e+04 4.37e-04 2.13e+00 -3.8 2.00e-01 - 7.95e-01 5.13e-01h 1
15 6.3352718e+04 1.22e-04 8.47e-02 -3.8 2.47e-01 - 1.00e+00 9.79e-01h 1
16 6.3352941e+04 1.86e-06 2.89e-05 -3.8 9.06e-03 - 1.00e+00 1.00e+00h 1
17 6.3352216e+04 3.36e-07 5.07e-04 -5.7 9.37e-03 - 9.90e-01 9.93e-01h 1
18 6.3352210e+04 1.17e-09 2.51e-08 -5.7 1.81e-04 - 1.00e+00 1.00e+00h 1
19 6.3352201e+04 5.65e-11 8.24e-10 -8.6 1.04e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 19
(scaled) (unscaled)
Objective...............: 4.8732462373895544e+02 6.3352201086064204e+04
Dual infeasibility......: 8.2365533028260471e-10 1.0707519293673861e-07
Constraint violation....: 5.6516707980236447e-11 5.6516707980236447e-11
Variable bound violation: 3.9922449346363464e-08 3.9922449346363464e-08
Complementarity.........: 3.7502813811802746e-09 4.8753657955343567e-07
Overall NLP error.......: 3.7502813811802746e-09 4.8753657955343567e-07
Number of objective function evaluations = 21
Number of objective gradient evaluations = 20
Number of equality constraint evaluations = 21
Number of inequality constraint evaluations = 21
Number of equality constraint Jacobian evaluations = 20
Number of inequality constraint Jacobian evaluations = 20
Number of Lagrangian Hessian evaluations = 19
Total seconds in IPOPT = 67.408
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 979
Number of nonzeros in inequality constraint Jacobian.: 304
Number of nonzeros in Lagrangian Hessian.............: 1543
Total number of variables............................: 265
variables with only lower bounds: 0
variables with lower and upper bounds: 241
variables with only upper bounds: 0
Total number of equality constraints.................: 201
Total number of inequality constraints...............: 152
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 152
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 4.0097983e+04 2.52e+00 4.56e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 5.6173148e+04 1.56e+00 6.27e+01 -1.0 2.38e+00 - 1.00e-02 3.79e-01h 1
2 5.3163376e+04 1.35e+00 5.43e+01 -1.0 6.42e+00 - 1.34e-02 1.39e-01f 1
3 5.2196944e+04 1.33e+00 5.02e+01 -1.0 8.86e+00 - 3.43e-01 1.34e-02f 1
4 5.3182043e+04 1.06e+00 3.73e+01 -1.0 7.58e+00 - 7.33e-01 2.03e-01h 1
5 5.5137462e+04 3.87e-01 1.31e+01 -1.0 9.48e+00 - 9.74e-01 6.34e-01H 1
6 5.4942906e+04 3.18e-01 1.19e+01 -1.0 1.03e+01 - 4.67e-01 1.79e-01f 1
7 5.5363333e+04 2.73e-01 1.12e+01 -1.0 7.18e+00 - 1.00e+00 1.42e-01h 1
8 5.5935463e+04 2.33e-01 6.62e+00 -1.0 3.08e+00 - 2.59e-01 1.44e-01h 1
9 6.2388698e+04 6.41e-02 5.12e+00 -1.0 1.40e+00 - 6.92e-01 8.82e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 6.3791569e+04 2.81e-03 8.67e-01 -1.0 1.80e+00 - 7.56e-01 1.00e+00h 1
11 6.3501031e+04 8.45e-04 6.21e-01 -1.7 1.69e-01 - 8.51e-01 1.00e+00f 1
12 6.3454928e+04 1.01e-03 1.25e-02 -1.7 1.02e-01 - 1.00e+00 1.00e+00h 1
13 6.3378078e+04 8.53e-04 1.25e-01 -3.8 1.49e-01 - 8.03e-01 7.30e-01f 1
14 6.3364846e+04 4.37e-04 2.13e+00 -3.8 2.00e-01 - 7.95e-01 5.13e-01h 1
15 6.3352718e+04 1.22e-04 8.47e-02 -3.8 2.47e-01 - 1.00e+00 9.79e-01h 1
16 6.3352941e+04 1.86e-06 2.89e-05 -3.8 9.06e-03 - 1.00e+00 1.00e+00h 1
17 6.3352216e+04 3.36e-07 5.07e-04 -5.7 9.37e-03 - 9.90e-01 9.93e-01h 1
18 6.3352210e+04 1.17e-09 2.51e-08 -5.7 1.81e-04 - 1.00e+00 1.00e+00h 1
19 6.3352201e+04 5.65e-11 8.24e-10 -8.6 1.04e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 19
(scaled) (unscaled)
Objective...............: 4.8732462373895544e+02 6.3352201086064204e+04
Dual infeasibility......: 8.2365533028260471e-10 1.0707519293673861e-07
Constraint violation....: 5.6516707980236447e-11 5.6516707980236447e-11
Variable bound violation: 3.9922449346363464e-08 3.9922449346363464e-08
Complementarity.........: 3.7502813811802746e-09 4.8753657955343567e-07
Overall NLP error.......: 3.7502813811802746e-09 4.8753657955343567e-07
Number of objective function evaluations = 21
Number of objective gradient evaluations = 20
Number of equality constraint evaluations = 21
Number of inequality constraint evaluations = 21
Number of equality constraint Jacobian evaluations = 20
Number of inequality constraint Jacobian evaluations = 20
Number of Lagrangian Hessian evaluations = 19
Total seconds in IPOPT = 0.047
EXIT: Optimal Solution Found.
file = "/tmp/jl_3GWXAw/pglib_opf_case2736sp_k.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2737sop_k.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2742_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2746wop_k.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2746wp_k.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2848_rte.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2853_sdet.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2868_rte.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case2869_pegase.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case30000_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case300_ieee.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case3012wp_k.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case3022_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case30_as.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 999
Number of nonzeros in inequality constraint Jacobian.: 328
Number of nonzeros in Lagrangian Hessian.............: 1634
Total number of variables............................: 236
variables with only lower bounds: 0
variables with lower and upper bounds: 206
variables with only upper bounds: 0
Total number of equality constraints.................: 225
Total number of inequality constraints...............: 164
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 164
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.9330612e+02 7.89e-01 6.98e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 7.3680988e+02 1.06e-01 4.80e+01 -1.0 7.66e-01 - 1.76e-02 8.65e-01h 1
2 7.6515454e+02 5.53e-02 2.51e+01 -1.0 9.70e-01 - 7.06e-01 4.79e-01h 1
3 8.0390995e+02 9.81e-03 3.83e+00 -1.0 6.95e-01 - 1.00e+00 1.00e+00h 1
4 8.0419681e+02 2.85e-04 1.48e-01 -1.0 1.06e-01 - 1.00e+00 1.00e+00h 1
5 8.0355508e+02 1.29e-04 2.65e-02 -1.7 7.56e-02 - 1.00e+00 1.00e+00f 1
6 8.0322260e+02 1.45e-04 2.45e-02 -2.5 4.50e-02 - 9.81e-01 1.00e+00f 1
7 8.0312215e+02 2.25e-04 7.52e-02 -3.8 4.76e-02 - 8.87e-01 9.26e-01h 1
8 8.0312831e+02 3.92e-05 5.43e-04 -3.8 1.60e-02 - 1.00e+00 1.00e+00h 1
9 8.0312738e+02 1.51e-06 2.40e-04 -5.7 4.36e-03 - 1.00e+00 9.98e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.0312733e+02 3.89e-09 4.27e-07 -5.7 3.29e-04 - 1.00e+00 1.00e+00f 1
11 8.0312731e+02 7.14e-11 1.82e-09 -8.6 2.73e-05 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 11
(scaled) (unscaled)
Objective...............: 2.4711609550316643e+02 8.0312731038529091e+02
Dual infeasibility......: 1.8152626068967948e-09 5.8996034724145829e-09
Constraint violation....: 7.1401801138293308e-11 7.1401801138293308e-11
Variable bound violation: 1.0369035186030828e-08 1.0369035186030828e-08
Complementarity.........: 3.8105847358648169e-09 1.2384400391560654e-08
Overall NLP error.......: 3.8105847358648169e-09 1.2384400391560654e-08
Number of objective function evaluations = 12
Number of objective gradient evaluations = 12
Number of equality constraint evaluations = 12
Number of inequality constraint evaluations = 12
Number of equality constraint Jacobian evaluations = 12
Number of inequality constraint Jacobian evaluations = 12
Number of Lagrangian Hessian evaluations = 11
Total seconds in IPOPT = 93.171
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 999
Number of nonzeros in inequality constraint Jacobian.: 328
Number of nonzeros in Lagrangian Hessian.............: 1634
Total number of variables............................: 236
variables with only lower bounds: 0
variables with lower and upper bounds: 206
variables with only upper bounds: 0
Total number of equality constraints.................: 225
Total number of inequality constraints...............: 164
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 164
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.9330612e+02 7.89e-01 6.98e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 7.3680988e+02 1.06e-01 4.80e+01 -1.0 7.66e-01 - 1.76e-02 8.65e-01h 1
2 7.6515454e+02 5.53e-02 2.51e+01 -1.0 9.70e-01 - 7.06e-01 4.79e-01h 1
3 8.0390995e+02 9.81e-03 3.83e+00 -1.0 6.95e-01 - 1.00e+00 1.00e+00h 1
4 8.0419681e+02 2.85e-04 1.48e-01 -1.0 1.06e-01 - 1.00e+00 1.00e+00h 1
5 8.0355508e+02 1.29e-04 2.65e-02 -1.7 7.56e-02 - 1.00e+00 1.00e+00f 1
6 8.0322260e+02 1.45e-04 2.45e-02 -2.5 4.50e-02 - 9.81e-01 1.00e+00f 1
7 8.0312215e+02 2.25e-04 7.52e-02 -3.8 4.76e-02 - 8.87e-01 9.26e-01h 1
8 8.0312831e+02 3.92e-05 5.43e-04 -3.8 1.60e-02 - 1.00e+00 1.00e+00h 1
9 8.0312738e+02 1.51e-06 2.40e-04 -5.7 4.36e-03 - 1.00e+00 9.98e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.0312733e+02 3.89e-09 4.27e-07 -5.7 3.29e-04 - 1.00e+00 1.00e+00f 1
11 8.0312731e+02 7.14e-11 1.82e-09 -8.6 2.73e-05 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 11
(scaled) (unscaled)
Objective...............: 2.4711609550316643e+02 8.0312731038529091e+02
Dual infeasibility......: 1.8152626068967948e-09 5.8996034724145829e-09
Constraint violation....: 7.1401801138293308e-11 7.1401801138293308e-11
Variable bound violation: 1.0369035186030828e-08 1.0369035186030828e-08
Complementarity.........: 3.8105847358648169e-09 1.2384400391560654e-08
Overall NLP error.......: 3.8105847358648169e-09 1.2384400391560654e-08
Number of objective function evaluations = 12
Number of objective gradient evaluations = 12
Number of equality constraint evaluations = 12
Number of inequality constraint evaluations = 12
Number of equality constraint Jacobian evaluations = 12
Number of inequality constraint Jacobian evaluations = 12
Number of Lagrangian Hessian evaluations = 11
Total seconds in IPOPT = 0.034
EXIT: Optimal Solution Found.
file = "/tmp/jl_3GWXAw/pglib_opf_case30_ieee.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 995
Number of nonzeros in inequality constraint Jacobian.: 328
Number of nonzeros in Lagrangian Hessian.............: 1628
Total number of variables............................: 232
variables with only lower bounds: 0
variables with lower and upper bounds: 202
variables with only upper bounds: 0
Total number of equality constraints.................: 225
Total number of inequality constraints...............: 164
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 164
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.6429132e+01 9.42e-01 1.81e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 7.3805974e+03 1.92e-01 1.37e+02 -1.0 1.73e+00 - 5.86e-03 7.96e-01H 1
2 7.0362117e+03 7.70e-02 5.47e+01 -1.0 1.62e+00 - 2.07e-02 6.00e-01f 1
3 7.0544217e+03 7.45e-02 5.34e+01 -1.0 7.99e-01 - 9.68e-01 3.19e-02h 1
4 8.2288089e+03 5.15e-04 1.49e+00 -1.0 3.21e-01 - 1.00e+00 1.00e+00h 1
5 8.2390074e+03 3.30e-05 1.34e-01 -1.0 2.16e-02 - 1.00e+00 1.00e+00h 1
6 8.2178583e+03 7.40e-05 4.92e-01 -2.5 2.35e-02 - 8.48e-01 9.57e-01f 1
7 8.2101534e+03 7.44e-05 6.45e+00 -2.5 4.92e-02 - 1.00e+00 7.43e-01f 1
8 8.2093612e+03 5.58e-06 6.10e-02 -2.5 2.33e-02 - 1.00e+00 1.00e+00f 1
9 8.2094471e+03 1.15e-07 8.09e-03 -2.5 1.99e-03 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.2085399e+03 5.53e-06 8.48e-04 -3.8 8.62e-03 - 1.00e+00 1.00e+00f 1
11 8.2085221e+03 8.56e-06 1.69e+01 -5.7 1.49e-03 - 8.58e-01 4.60e-01h 1
12 8.2085142e+03 1.44e-06 1.47e-02 -5.7 1.50e-03 - 1.00e+00 1.00e+00h 1
13 8.2085158e+03 5.61e-08 7.72e-05 -5.7 4.49e-04 - 1.00e+00 1.00e+00h 1
14 8.2085159e+03 2.07e-09 5.33e-06 -5.7 2.37e-04 - 1.00e+00 1.00e+00h 1
15 8.2085154e+03 5.30e-09 2.50e-03 -8.6 8.83e-05 - 9.80e-01 1.00e+00h 1
16 8.2085154e+03 4.62e-09 2.33e-03 -8.6 1.11e-05 - 1.00e+00 1.25e-01f 4
17 8.2085154e+03 3.63e-12 1.37e-08 -8.6 9.90e-06 - 1.00e+00 1.00e+00h 1
18 8.2085154e+03 7.11e-15 2.54e-12 -9.0 4.10e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 18
(scaled) (unscaled)
Objective...............: 1.5730473122733949e+02 8.2085154403067609e+03
Dual infeasibility......: 2.5375810559200892e-12 1.3241669920561030e-10
Constraint violation....: 7.1054273576010019e-15 7.1054273576010019e-15
Variable bound violation: 1.0549795703695963e-08 1.0549795703695963e-08
Complementarity.........: 9.0917662089786050e-10 4.7442885362553868e-08
Overall NLP error.......: 9.0917662089786050e-10 4.7442885362553868e-08
Number of objective function evaluations = 23
Number of objective gradient evaluations = 19
Number of equality constraint evaluations = 23
Number of inequality constraint evaluations = 23
Number of equality constraint Jacobian evaluations = 19
Number of inequality constraint Jacobian evaluations = 19
Number of Lagrangian Hessian evaluations = 18
Total seconds in IPOPT = 92.976
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 995
Number of nonzeros in inequality constraint Jacobian.: 328
Number of nonzeros in Lagrangian Hessian.............: 1628
Total number of variables............................: 232
variables with only lower bounds: 0
variables with lower and upper bounds: 202
variables with only upper bounds: 0
Total number of equality constraints.................: 225
Total number of inequality constraints...............: 164
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 164
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.6429132e+01 9.42e-01 1.81e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 7.3805974e+03 1.92e-01 1.37e+02 -1.0 1.73e+00 - 5.86e-03 7.96e-01H 1
2 7.0362117e+03 7.70e-02 5.47e+01 -1.0 1.62e+00 - 2.07e-02 6.00e-01f 1
3 7.0544217e+03 7.45e-02 5.34e+01 -1.0 7.99e-01 - 9.68e-01 3.19e-02h 1
4 8.2288089e+03 5.15e-04 1.49e+00 -1.0 3.21e-01 - 1.00e+00 1.00e+00h 1
5 8.2390074e+03 3.30e-05 1.34e-01 -1.0 2.16e-02 - 1.00e+00 1.00e+00h 1
6 8.2178583e+03 7.40e-05 4.92e-01 -2.5 2.35e-02 - 8.48e-01 9.57e-01f 1
7 8.2101534e+03 7.44e-05 6.45e+00 -2.5 4.92e-02 - 1.00e+00 7.43e-01f 1
8 8.2093612e+03 5.58e-06 6.10e-02 -2.5 2.33e-02 - 1.00e+00 1.00e+00f 1
9 8.2094471e+03 1.15e-07 8.09e-03 -2.5 1.99e-03 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.2085399e+03 5.53e-06 8.48e-04 -3.8 8.62e-03 - 1.00e+00 1.00e+00f 1
11 8.2085221e+03 8.56e-06 1.69e+01 -5.7 1.49e-03 - 8.58e-01 4.60e-01h 1
12 8.2085142e+03 1.44e-06 1.47e-02 -5.7 1.50e-03 - 1.00e+00 1.00e+00h 1
13 8.2085158e+03 5.61e-08 7.72e-05 -5.7 4.49e-04 - 1.00e+00 1.00e+00h 1
14 8.2085159e+03 2.07e-09 5.33e-06 -5.7 2.37e-04 - 1.00e+00 1.00e+00h 1
15 8.2085154e+03 5.30e-09 2.50e-03 -8.6 8.83e-05 - 9.80e-01 1.00e+00h 1
16 8.2085154e+03 4.62e-09 2.33e-03 -8.6 1.11e-05 - 1.00e+00 1.25e-01f 4
17 8.2085154e+03 3.63e-12 1.37e-08 -8.6 9.90e-06 - 1.00e+00 1.00e+00h 1
18 8.2085154e+03 7.11e-15 2.54e-12 -9.0 4.10e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 18
(scaled) (unscaled)
Objective...............: 1.5730473122733949e+02 8.2085154403067609e+03
Dual infeasibility......: 2.5375810559200892e-12 1.3241669920561030e-10
Constraint violation....: 7.1054273576010019e-15 7.1054273576010019e-15
Variable bound violation: 1.0549795703695963e-08 1.0549795703695963e-08
Complementarity.........: 9.0917662089786050e-10 4.7442885362553868e-08
Overall NLP error.......: 9.0917662089786050e-10 4.7442885362553868e-08
Number of objective function evaluations = 23
Number of objective gradient evaluations = 19
Number of equality constraint evaluations = 23
Number of inequality constraint evaluations = 23
Number of equality constraint Jacobian evaluations = 19
Number of inequality constraint Jacobian evaluations = 19
Number of Lagrangian Hessian evaluations = 18
Total seconds in IPOPT = 0.031
EXIT: Optimal Solution Found.
file = "/tmp/jl_3GWXAw/pglib_opf_case3120sp_k.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case3375wp_k.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case3970_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case39_epri.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 1125
Number of nonzeros in inequality constraint Jacobian.: 368
Number of nonzeros in Lagrangian Hessian.............: 1832
Total number of variables............................: 282
variables with only lower bounds: 0
variables with lower and upper bounds: 243
variables with only upper bounds: 0
Total number of equality constraints.................: 263
Total number of inequality constraints...............: 184
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 184
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.3768629e+02 1.10e+01 1.25e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.1096016e+04 1.02e+01 6.03e+01 -1.0 6.92e+00 - 1.45e-03 7.43e-02h 2
2 2.0586599e+04 9.35e+00 5.53e+01 -1.0 1.21e+01 - 5.88e-02 8.41e-02h 4
3 2.8045737e+04 8.65e+00 5.11e+01 -1.0 3.53e+01 - 1.19e-01 7.49e-02h 4
4 3.4162191e+04 8.06e+00 4.76e+01 -1.0 5.29e+01 - 2.03e-01 6.79e-02h 4
5 4.4158466e+04 7.09e+00 4.18e+01 -1.0 6.46e+01 - 2.50e-01 1.21e-01h 3
6 5.4914496e+04 6.03e+00 3.55e+01 -1.0 7.52e+01 - 2.65e-01 1.50e-01h 2
7 6.6043801e+04 4.95e+00 2.90e+01 -1.0 7.38e+01 - 5.73e-01 1.79e-01h 1
8 7.6876929e+04 4.05e+00 2.38e+01 -1.0 5.15e+01 - 5.38e-01 1.80e-01h 1
9 9.6582595e+04 2.65e+00 1.55e+01 -1.0 5.05e+01 - 3.04e-01 3.46e-01H 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.1317794e+05 1.47e+00 8.54e+00 -1.0 6.29e+01 - 3.50e-01 4.46e-01H 1
11 1.1373986e+05 1.43e+00 1.46e+01 -1.0 3.33e+01 - 8.90e-01 2.45e-02h 1
12 1.2662465e+05 6.62e-01 9.96e+00 -1.0 2.83e+01 - 1.00e+00 5.38e-01h 1
13 1.3081352e+05 4.07e-01 4.65e+00 -1.0 5.24e+01 - 8.69e-01 3.86e-01h 1
14 1.3171708e+05 3.56e-01 9.76e+00 -1.0 1.01e+01 - 3.50e-01 1.25e-01h 1
15 1.3630115e+05 1.14e-01 2.18e+00 -1.0 6.05e+00 - 1.00e+00 6.81e-01h 1
16 1.3815859e+05 2.30e-02 7.46e-01 -1.0 3.59e+00 - 8.11e-01 8.46e-01h 1
17 1.3844499e+05 4.46e-03 1.00e-01 -1.7 1.19e+00 - 9.76e-01 1.00e+00h 1
18 1.3842260e+05 3.88e-03 1.28e+00 -2.5 5.82e-01 - 1.00e+00 6.00e-01h 1
19 1.3841745e+05 2.48e-03 9.52e+00 -2.5 5.85e-01 - 6.99e-01 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.3841745e+05 3.85e-05 7.32e-04 -2.5 5.45e-02 - 1.00e+00 1.00e+00h 1
21 1.3841575e+05 4.57e-05 5.16e-02 -3.8 1.08e-01 - 9.11e-01 9.17e-01h 1
22 1.3841566e+05 7.70e-06 2.95e-04 -3.8 1.89e-02 - 1.00e+00 1.00e+00h 1
23 1.3841557e+05 3.47e-06 1.17e-01 -5.7 9.00e-03 - 9.92e-01 9.04e-01h 1
24 1.3841556e+05 1.69e-07 2.44e-06 -5.7 1.56e-03 - 1.00e+00 1.00e+00h 1
25 1.3841556e+05 1.86e-10 4.07e-06 -8.6 7.90e-05 - 1.00e+00 1.00e+00h 1
26 1.3841556e+05 5.68e-14 1.61e-11 -8.6 5.30e-08 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 26
(scaled) (unscaled)
Objective...............: 3.9723627718148382e+03 1.3841556265037847e+05
Dual infeasibility......: 1.6144059141495091e-11 5.6253397735628296e-10
Constraint violation....: 4.4075854077618715e-14 5.6843418860808015e-14
Variable bound violation: 1.0982981990537155e-07 1.0982981990537155e-07
Complementarity.........: 2.5059244715281759e-09 8.7318043595362940e-08
Overall NLP error.......: 2.5059244715281759e-09 8.7318043595362940e-08
Number of objective function evaluations = 55
Number of objective gradient evaluations = 27
Number of equality constraint evaluations = 55
Number of inequality constraint evaluations = 55
Number of equality constraint Jacobian evaluations = 27
Number of inequality constraint Jacobian evaluations = 27
Number of Lagrangian Hessian evaluations = 26
Total seconds in IPOPT = 133.898
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 1125
Number of nonzeros in inequality constraint Jacobian.: 368
Number of nonzeros in Lagrangian Hessian.............: 1832
Total number of variables............................: 282
variables with only lower bounds: 0
variables with lower and upper bounds: 243
variables with only upper bounds: 0
Total number of equality constraints.................: 263
Total number of inequality constraints...............: 184
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 184
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 2.3768629e+02 1.10e+01 1.25e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.1096016e+04 1.02e+01 6.03e+01 -1.0 6.92e+00 - 1.45e-03 7.43e-02h 2
2 2.0586599e+04 9.35e+00 5.53e+01 -1.0 1.21e+01 - 5.88e-02 8.41e-02h 4
3 2.8045737e+04 8.65e+00 5.11e+01 -1.0 3.53e+01 - 1.19e-01 7.49e-02h 4
4 3.4162191e+04 8.06e+00 4.76e+01 -1.0 5.29e+01 - 2.03e-01 6.79e-02h 4
5 4.4158466e+04 7.09e+00 4.18e+01 -1.0 6.46e+01 - 2.50e-01 1.21e-01h 3
6 5.4914496e+04 6.03e+00 3.55e+01 -1.0 7.52e+01 - 2.65e-01 1.50e-01h 2
7 6.6043801e+04 4.95e+00 2.90e+01 -1.0 7.38e+01 - 5.73e-01 1.79e-01h 1
8 7.6876929e+04 4.05e+00 2.38e+01 -1.0 5.15e+01 - 5.38e-01 1.80e-01h 1
9 9.6582595e+04 2.65e+00 1.55e+01 -1.0 5.05e+01 - 3.04e-01 3.46e-01H 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.1317794e+05 1.47e+00 8.54e+00 -1.0 6.29e+01 - 3.50e-01 4.46e-01H 1
11 1.1373986e+05 1.43e+00 1.46e+01 -1.0 3.33e+01 - 8.90e-01 2.45e-02h 1
12 1.2662465e+05 6.62e-01 9.96e+00 -1.0 2.83e+01 - 1.00e+00 5.38e-01h 1
13 1.3081352e+05 4.07e-01 4.65e+00 -1.0 5.24e+01 - 8.69e-01 3.86e-01h 1
14 1.3171708e+05 3.56e-01 9.76e+00 -1.0 1.01e+01 - 3.50e-01 1.25e-01h 1
15 1.3630115e+05 1.14e-01 2.18e+00 -1.0 6.05e+00 - 1.00e+00 6.81e-01h 1
16 1.3815859e+05 2.30e-02 7.46e-01 -1.0 3.59e+00 - 8.11e-01 8.46e-01h 1
17 1.3844499e+05 4.46e-03 1.00e-01 -1.7 1.19e+00 - 9.76e-01 1.00e+00h 1
18 1.3842260e+05 3.88e-03 1.28e+00 -2.5 5.82e-01 - 1.00e+00 6.00e-01h 1
19 1.3841745e+05 2.48e-03 9.52e+00 -2.5 5.85e-01 - 6.99e-01 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.3841745e+05 3.85e-05 7.32e-04 -2.5 5.45e-02 - 1.00e+00 1.00e+00h 1
21 1.3841575e+05 4.57e-05 5.16e-02 -3.8 1.08e-01 - 9.11e-01 9.17e-01h 1
22 1.3841566e+05 7.70e-06 2.95e-04 -3.8 1.89e-02 - 1.00e+00 1.00e+00h 1
23 1.3841557e+05 3.47e-06 1.17e-01 -5.7 9.00e-03 - 9.92e-01 9.04e-01h 1
24 1.3841556e+05 1.69e-07 2.44e-06 -5.7 1.56e-03 - 1.00e+00 1.00e+00h 1
25 1.3841556e+05 1.86e-10 4.07e-06 -8.6 7.90e-05 - 1.00e+00 1.00e+00h 1
26 1.3841556e+05 5.68e-14 1.61e-11 -8.6 5.30e-08 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 26
(scaled) (unscaled)
Objective...............: 3.9723627718148382e+03 1.3841556265037847e+05
Dual infeasibility......: 1.6144059141495091e-11 5.6253397735628296e-10
Constraint violation....: 4.4075854077618715e-14 5.6843418860808015e-14
Variable bound violation: 1.0982981990537155e-07 1.0982981990537155e-07
Complementarity.........: 2.5059244715281759e-09 8.7318043595362940e-08
Overall NLP error.......: 2.5059244715281759e-09 8.7318043595362940e-08
Number of objective function evaluations = 55
Number of objective gradient evaluations = 27
Number of equality constraint evaluations = 55
Number of inequality constraint evaluations = 55
Number of equality constraint Jacobian evaluations = 27
Number of inequality constraint Jacobian evaluations = 27
Number of Lagrangian Hessian evaluations = 26
Total seconds in IPOPT = 0.062
EXIT: Optimal Solution Found.
file = "/tmp/jl_3GWXAw/pglib_opf_case3_lmbd.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 78
Number of nonzeros in inequality constraint Jacobian.: 24
Number of nonzeros in Lagrangian Hessian.............: 122
Total number of variables............................: 23
variables with only lower bounds: 0
variables with lower and upper bounds: 20
variables with only upper bounds: 0
Total number of equality constraints.................: 19
Total number of inequality constraints...............: 12
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 12
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.3949934e+00 1.09e+00 1.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.1048421e+03 4.62e-01 1.04e+02 -1.0 1.81e+00 - 5.77e-03 5.76e-01h 1
2 4.4503068e+03 1.36e-01 3.09e+01 -1.0 8.29e-01 - 8.15e-01 7.05e-01h 1
3 4.6140270e+03 1.18e-01 2.67e+01 -1.0 2.99e-01 - 4.13e-01 1.37e-01h 1
4 4.9343698e+03 8.25e-02 3.85e+01 -1.0 4.36e-01 - 4.94e-01 2.98e-01h 1
5 5.4019378e+03 3.46e-02 2.51e+01 -1.0 3.62e-01 - 9.90e-01 5.81e-01h 1
6 5.4116196e+03 3.37e-02 1.26e+02 -1.0 1.45e-01 - 1.84e-01 2.70e-02h 1
7 5.6094774e+03 1.60e-02 5.13e+01 -1.0 2.53e-01 - 2.76e-01 5.25e-01h 1
8 5.7031738e+03 8.53e-03 8.21e+01 -1.0 1.43e-01 - 7.00e-01 4.67e-01h 1
9 5.8145457e+03 6.10e-04 2.19e+00 -1.0 5.77e-02 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 5.8146316e+03 2.55e-05 7.83e-03 -1.0 1.23e-02 - 1.00e+00 1.00e+00h 1
11 5.8127612e+03 1.60e-05 1.36e-02 -2.5 8.27e-03 - 1.00e+00 1.00e+00f 1
12 5.8126464e+03 2.60e-07 1.15e-04 -3.8 1.05e-03 - 1.00e+00 1.00e+00f 1
13 5.8126430e+03 1.32e-10 8.31e-08 -5.7 2.50e-05 - 1.00e+00 1.00e+00h 1
14 5.8126429e+03 7.77e-15 6.25e-12 -8.6 1.78e-07 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 14
(scaled) (unscaled)
Objective...............: 1.1625285870072362e+03 5.8126429350361805e+03
Dual infeasibility......: 6.2541516535614795e-12 3.1270758267807398e-11
Constraint violation....: 7.7715611723760958e-15 7.7715611723760958e-15
Variable bound violation: 1.0911841874516881e-08 1.0911841874516881e-08
Complementarity.........: 2.5102170848799279e-09 1.2551085424399639e-08
Overall NLP error.......: 2.5102170848799279e-09 1.2551085424399639e-08
Number of objective function evaluations = 15
Number of objective gradient evaluations = 15
Number of equality constraint evaluations = 15
Number of inequality constraint evaluations = 15
Number of equality constraint Jacobian evaluations = 15
Number of inequality constraint Jacobian evaluations = 15
Number of Lagrangian Hessian evaluations = 14
Total seconds in IPOPT = 0.007
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 78
Number of nonzeros in inequality constraint Jacobian.: 24
Number of nonzeros in Lagrangian Hessian.............: 122
Total number of variables............................: 23
variables with only lower bounds: 0
variables with lower and upper bounds: 20
variables with only upper bounds: 0
Total number of equality constraints.................: 19
Total number of inequality constraints...............: 12
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 12
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 6.3949934e+00 1.09e+00 1.67e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.1048421e+03 4.62e-01 1.04e+02 -1.0 1.81e+00 - 5.77e-03 5.76e-01h 1
2 4.4503068e+03 1.36e-01 3.09e+01 -1.0 8.29e-01 - 8.15e-01 7.05e-01h 1
3 4.6140270e+03 1.18e-01 2.67e+01 -1.0 2.99e-01 - 4.13e-01 1.37e-01h 1
4 4.9343698e+03 8.25e-02 3.85e+01 -1.0 4.36e-01 - 4.94e-01 2.98e-01h 1
5 5.4019378e+03 3.46e-02 2.51e+01 -1.0 3.62e-01 - 9.90e-01 5.81e-01h 1
6 5.4116196e+03 3.37e-02 1.26e+02 -1.0 1.45e-01 - 1.84e-01 2.70e-02h 1
7 5.6094774e+03 1.60e-02 5.13e+01 -1.0 2.53e-01 - 2.76e-01 5.25e-01h 1
8 5.7031738e+03 8.53e-03 8.21e+01 -1.0 1.43e-01 - 7.00e-01 4.67e-01h 1
9 5.8145457e+03 6.10e-04 2.19e+00 -1.0 5.77e-02 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 5.8146316e+03 2.55e-05 7.83e-03 -1.0 1.23e-02 - 1.00e+00 1.00e+00h 1
11 5.8127612e+03 1.60e-05 1.36e-02 -2.5 8.27e-03 - 1.00e+00 1.00e+00f 1
12 5.8126464e+03 2.60e-07 1.15e-04 -3.8 1.05e-03 - 1.00e+00 1.00e+00f 1
13 5.8126430e+03 1.32e-10 8.31e-08 -5.7 2.50e-05 - 1.00e+00 1.00e+00h 1
14 5.8126429e+03 7.77e-15 6.25e-12 -8.6 1.78e-07 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 14
(scaled) (unscaled)
Objective...............: 1.1625285870072362e+03 5.8126429350361805e+03
Dual infeasibility......: 6.2541516535614795e-12 3.1270758267807398e-11
Constraint violation....: 7.7715611723760958e-15 7.7715611723760958e-15
Variable bound violation: 1.0911841874516881e-08 1.0911841874516881e-08
Complementarity.........: 2.5102170848799279e-09 1.2551085424399639e-08
Overall NLP error.......: 2.5102170848799279e-09 1.2551085424399639e-08
Number of objective function evaluations = 15
Number of objective gradient evaluations = 15
Number of equality constraint evaluations = 15
Number of inequality constraint evaluations = 15
Number of equality constraint Jacobian evaluations = 15
Number of inequality constraint Jacobian evaluations = 15
Number of Lagrangian Hessian evaluations = 14
Total seconds in IPOPT = 0.007
EXIT: Optimal Solution Found.
file = "/tmp/jl_3GWXAw/pglib_opf_case4020_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case4601_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case4619_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case4661_sdet.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case4837_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case4917_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case500_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case5658_epigrids.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case57_ieee.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 1935
Number of nonzeros in inequality constraint Jacobian.: 640
Number of nonzeros in Lagrangian Hessian.............: 3167
Total number of variables............................: 445
variables with only lower bounds: 0
variables with lower and upper bounds: 388
variables with only upper bounds: 0
Total number of equality constraints.................: 435
Total number of inequality constraints...............: 320
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 320
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0503586e+02 3.76e+00 1.91e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.0716549e+04 2.65e+00 1.19e+02 -1.0 3.70e+00 - 2.74e-03 2.95e-01h 1
2 1.3984057e+04 2.15e+00 9.71e+01 -1.0 7.02e+00 - 5.91e-02 1.90e-01h 1
3 1.4673527e+04 2.08e+00 7.81e+01 -1.0 3.90e+00 - 3.67e-01 2.99e-02h 1
4 2.0340384e+04 1.57e+00 5.90e+01 -1.0 4.22e+00 - 7.79e-01 2.46e-01h 1
5 3.5210269e+04 2.14e-01 4.79e+01 -1.0 4.88e+00 - 7.60e-01 8.64e-01h 1
6 3.6360055e+04 1.01e-01 2.37e+01 -1.0 1.06e+01 - 7.33e-01 5.27e-01h 1
7 3.7555239e+04 1.23e-02 1.55e+00 -1.0 6.75e+00 - 1.00e+00 1.00e+00h 1
8 3.7614438e+04 3.64e-04 1.91e-02 -1.0 1.25e+00 - 1.00e+00 1.00e+00h 1
9 3.7591308e+04 4.39e-04 3.47e+00 -2.5 1.26e+00 - 8.64e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 3.7590571e+04 8.45e-05 2.31e+00 -2.5 1.37e-01 - 9.45e-01 8.36e-01h 1
11 3.7590149e+04 1.43e-05 1.36e-03 -2.5 2.81e-02 - 1.00e+00 1.00e+00f 1
12 3.7589396e+04 2.20e-06 1.90e-02 -3.8 4.24e-02 - 9.89e-01 1.00e+00h 1
13 3.7589383e+04 8.07e-09 2.05e-06 -3.8 5.69e-04 - 1.00e+00 1.00e+00h 1
14 3.7589339e+04 7.59e-09 1.23e-06 -5.7 2.32e-03 - 1.00e+00 1.00e+00h 1
15 3.7589338e+04 1.85e-12 2.72e-10 -8.6 2.92e-05 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.0107655336327783e+03 3.7589338204193191e+04
Dual infeasibility......: 2.7150766440745813e-10 1.0097092829988010e-08
Constraint violation....: 1.8545165403338615e-12 1.8545165403338615e-12
Variable bound violation: 2.4448082225347889e-08 2.4448082225347889e-08
Complementarity.........: 2.6288227592952003e-09 9.7763234390151265e-08
Overall NLP error.......: 2.6288227592952003e-09 9.7763234390151265e-08
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total seconds in IPOPT = 540.553
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 1935
Number of nonzeros in inequality constraint Jacobian.: 640
Number of nonzeros in Lagrangian Hessian.............: 3167
Total number of variables............................: 445
variables with only lower bounds: 0
variables with lower and upper bounds: 388
variables with only upper bounds: 0
Total number of equality constraints.................: 435
Total number of inequality constraints...............: 320
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 320
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0503586e+02 3.76e+00 1.91e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.0716549e+04 2.65e+00 1.19e+02 -1.0 3.70e+00 - 2.74e-03 2.95e-01h 1
2 1.3984057e+04 2.15e+00 9.71e+01 -1.0 7.02e+00 - 5.91e-02 1.90e-01h 1
3 1.4673527e+04 2.08e+00 7.81e+01 -1.0 3.90e+00 - 3.67e-01 2.99e-02h 1
4 2.0340384e+04 1.57e+00 5.90e+01 -1.0 4.22e+00 - 7.79e-01 2.46e-01h 1
5 3.5210269e+04 2.14e-01 4.79e+01 -1.0 4.88e+00 - 7.60e-01 8.64e-01h 1
6 3.6360055e+04 1.01e-01 2.37e+01 -1.0 1.06e+01 - 7.33e-01 5.27e-01h 1
7 3.7555239e+04 1.23e-02 1.55e+00 -1.0 6.75e+00 - 1.00e+00 1.00e+00h 1
8 3.7614438e+04 3.64e-04 1.91e-02 -1.0 1.25e+00 - 1.00e+00 1.00e+00h 1
9 3.7591308e+04 4.39e-04 3.47e+00 -2.5 1.26e+00 - 8.64e-01 1.00e+00f 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 3.7590571e+04 8.45e-05 2.31e+00 -2.5 1.37e-01 - 9.45e-01 8.36e-01h 1
11 3.7590149e+04 1.43e-05 1.36e-03 -2.5 2.81e-02 - 1.00e+00 1.00e+00f 1
12 3.7589396e+04 2.20e-06 1.90e-02 -3.8 4.24e-02 - 9.89e-01 1.00e+00h 1
13 3.7589383e+04 8.07e-09 2.05e-06 -3.8 5.69e-04 - 1.00e+00 1.00e+00h 1
14 3.7589339e+04 7.59e-09 1.23e-06 -5.7 2.32e-03 - 1.00e+00 1.00e+00h 1
15 3.7589338e+04 1.85e-12 2.72e-10 -8.6 2.92e-05 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 15
(scaled) (unscaled)
Objective...............: 1.0107655336327783e+03 3.7589338204193191e+04
Dual infeasibility......: 2.7150766440745813e-10 1.0097092829988010e-08
Constraint violation....: 1.8545165403338615e-12 1.8545165403338615e-12
Variable bound violation: 2.4448082225347889e-08 2.4448082225347889e-08
Complementarity.........: 2.6288227592952003e-09 9.7763234390151265e-08
Overall NLP error.......: 2.6288227592952003e-09 9.7763234390151265e-08
Number of objective function evaluations = 16
Number of objective gradient evaluations = 16
Number of equality constraint evaluations = 16
Number of inequality constraint evaluations = 16
Number of equality constraint Jacobian evaluations = 16
Number of inequality constraint Jacobian evaluations = 16
Number of Lagrangian Hessian evaluations = 15
Total seconds in IPOPT = 0.085
EXIT: Optimal Solution Found.
file = "/tmp/jl_3GWXAw/pglib_opf_case588_sdet.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case5_pjm.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 155
Number of nonzeros in inequality constraint Jacobian.: 48
Number of nonzeros in Lagrangian Hessian.............: 240
Total number of variables............................: 44
variables with only lower bounds: 0
variables with lower and upper bounds: 39
variables with only upper bounds: 0
Total number of equality constraints.................: 35
Total number of inequality constraints...............: 24
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 24
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0059989e+02 3.99e+00 2.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 8.3066305e+03 2.47e+00 1.01e+02 -1.0 2.78e+00 - 4.11e-03 3.82e-01h 1
2 6.7181372e+03 2.36e+00 9.62e+01 -1.0 1.60e+01 - 7.37e-02 4.44e-02f 1
3 6.6689587e+03 2.30e+00 9.34e+01 -1.0 1.30e+01 - 4.94e-01 2.40e-02f 1
4 6.5741805e+03 2.04e+00 8.25e+01 -1.0 1.29e+01 - 3.67e-01 1.12e-01f 2
5 6.8264259e+03 1.80e+00 7.10e+01 -1.0 1.23e+01 - 8.72e-01 1.20e-01h 2
6 8.8540136e+03 1.08e+00 4.20e+01 -1.0 9.14e+00 - 5.92e-01 4.00e-01h 1
7 1.0572806e+04 8.62e-01 3.58e+01 -1.0 2.94e+00 - 4.93e-01 2.00e-01h 1
8 1.7308577e+04 3.63e-02 1.46e+01 -1.0 2.41e+00 - 7.65e-01 9.58e-01h 1
9 1.7572869e+04 1.33e-02 1.10e+00 -1.0 2.11e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7590631e+04 1.68e-03 1.61e-01 -1.0 5.04e-01 - 1.00e+00 1.00e+00h 1
11 1.7558724e+04 5.24e-03 5.03e-01 -2.5 6.03e-01 - 8.35e-01 9.36e-01f 1
12 1.7553111e+04 3.34e-03 4.12e+00 -2.5 2.84e-01 - 1.00e+00 8.20e-01h 1
13 1.7552956e+04 3.24e-05 1.26e-02 -2.5 6.35e-02 - 1.00e+00 1.00e+00h 1
14 1.7551990e+04 1.35e-05 1.09e+00 -3.8 2.53e-02 - 1.00e+00 9.25e-01h 1
15 1.7551938e+04 4.46e-08 1.22e-02 -3.8 7.00e-03 - 1.00e+00 1.00e+00f 1
16 1.7551940e+04 2.35e-10 2.06e-04 -3.8 3.83e-04 - 1.00e+00 1.00e+00h 1
17 1.7551893e+04 1.75e-07 2.10e-01 -5.7 2.49e-03 - 1.00e+00 9.68e-01f 1
18 1.7551891e+04 6.80e-11 3.09e-05 -5.7 2.38e-04 - 1.00e+00 1.00e+00f 1
19 1.7551891e+04 3.06e-14 6.47e-10 -5.7 5.17e-07 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.7551891e+04 6.26e-12 3.03e-07 -8.6 3.52e-05 - 1.00e+00 1.00e+00f 1
21 1.7551891e+04 2.92e-14 3.65e-12 -8.6 3.33e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 21
(scaled) (unscaled)
Objective...............: 4.3879727248486864e+02 1.7551890899394744e+04
Dual infeasibility......: 3.6484809026628434e-12 1.4593923610651373e-10
Constraint violation....: 1.8856205485917602e-14 2.9171109972025988e-14
Variable bound violation: 2.9463905093507492e-08 2.9463905093507492e-08
Complementarity.........: 2.5059076126554735e-09 1.0023630450621893e-07
Overall NLP error.......: 2.5059076126554735e-09 1.0023630450621893e-07
Number of objective function evaluations = 28
Number of objective gradient evaluations = 22
Number of equality constraint evaluations = 28
Number of inequality constraint evaluations = 28
Number of equality constraint Jacobian evaluations = 22
Number of inequality constraint Jacobian evaluations = 22
Number of Lagrangian Hessian evaluations = 21
Total seconds in IPOPT = 0.012
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 155
Number of nonzeros in inequality constraint Jacobian.: 48
Number of nonzeros in Lagrangian Hessian.............: 240
Total number of variables............................: 44
variables with only lower bounds: 0
variables with lower and upper bounds: 39
variables with only upper bounds: 0
Total number of equality constraints.................: 35
Total number of inequality constraints...............: 24
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 24
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0059989e+02 3.99e+00 2.88e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 8.3066305e+03 2.47e+00 1.01e+02 -1.0 2.78e+00 - 4.11e-03 3.82e-01h 1
2 6.7181372e+03 2.36e+00 9.62e+01 -1.0 1.60e+01 - 7.37e-02 4.44e-02f 1
3 6.6689587e+03 2.30e+00 9.34e+01 -1.0 1.30e+01 - 4.94e-01 2.40e-02f 1
4 6.5741805e+03 2.04e+00 8.25e+01 -1.0 1.29e+01 - 3.67e-01 1.12e-01f 2
5 6.8264259e+03 1.80e+00 7.10e+01 -1.0 1.23e+01 - 8.72e-01 1.20e-01h 2
6 8.8540136e+03 1.08e+00 4.20e+01 -1.0 9.14e+00 - 5.92e-01 4.00e-01h 1
7 1.0572806e+04 8.62e-01 3.58e+01 -1.0 2.94e+00 - 4.93e-01 2.00e-01h 1
8 1.7308577e+04 3.63e-02 1.46e+01 -1.0 2.41e+00 - 7.65e-01 9.58e-01h 1
9 1.7572869e+04 1.33e-02 1.10e+00 -1.0 2.11e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7590631e+04 1.68e-03 1.61e-01 -1.0 5.04e-01 - 1.00e+00 1.00e+00h 1
11 1.7558724e+04 5.24e-03 5.03e-01 -2.5 6.03e-01 - 8.35e-01 9.36e-01f 1
12 1.7553111e+04 3.34e-03 4.12e+00 -2.5 2.84e-01 - 1.00e+00 8.20e-01h 1
13 1.7552956e+04 3.24e-05 1.26e-02 -2.5 6.35e-02 - 1.00e+00 1.00e+00h 1
14 1.7551990e+04 1.35e-05 1.09e+00 -3.8 2.53e-02 - 1.00e+00 9.25e-01h 1
15 1.7551938e+04 4.46e-08 1.22e-02 -3.8 7.00e-03 - 1.00e+00 1.00e+00f 1
16 1.7551940e+04 2.35e-10 2.06e-04 -3.8 3.83e-04 - 1.00e+00 1.00e+00h 1
17 1.7551893e+04 1.75e-07 2.10e-01 -5.7 2.49e-03 - 1.00e+00 9.68e-01f 1
18 1.7551891e+04 6.80e-11 3.09e-05 -5.7 2.38e-04 - 1.00e+00 1.00e+00f 1
19 1.7551891e+04 3.06e-14 6.47e-10 -5.7 5.17e-07 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.7551891e+04 6.26e-12 3.03e-07 -8.6 3.52e-05 - 1.00e+00 1.00e+00f 1
21 1.7551891e+04 2.92e-14 3.65e-12 -8.6 3.33e-08 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 21
(scaled) (unscaled)
Objective...............: 4.3879727248486864e+02 1.7551890899394744e+04
Dual infeasibility......: 3.6484809026628434e-12 1.4593923610651373e-10
Constraint violation....: 1.8856205485917602e-14 2.9171109972025988e-14
Variable bound violation: 2.9463905093507492e-08 2.9463905093507492e-08
Complementarity.........: 2.5059076126554735e-09 1.0023630450621893e-07
Overall NLP error.......: 2.5059076126554735e-09 1.0023630450621893e-07
Number of objective function evaluations = 28
Number of objective gradient evaluations = 22
Number of equality constraint evaluations = 28
Number of inequality constraint evaluations = 28
Number of equality constraint Jacobian evaluations = 22
Number of inequality constraint Jacobian evaluations = 22
Number of Lagrangian Hessian evaluations = 21
Total seconds in IPOPT = 0.011
EXIT: Optimal Solution Found.
file = "/tmp/jl_3GWXAw/pglib_opf_case60_c.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 2170
Number of nonzeros in inequality constraint Jacobian.: 704
Number of nonzeros in Lagrangian Hessian.............: 3460
Total number of variables............................: 517
variables with only lower bounds: 0
variables with lower and upper bounds: 457
variables with only upper bounds: 0
Total number of equality constraints.................: 473
Total number of inequality constraints...............: 352
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 352
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.8499996e+03 2.00e+01 1.41e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.3245588e+04 1.86e+01 3.73e+01 -1.0 7.57e+00 - 2.13e-03 7.03e-02h 3
2 1.7309771e+04 1.73e+01 3.47e+01 -1.0 1.53e+01 - 3.49e-02 6.78e-02h 1
3 1.8342944e+04 1.71e+01 3.43e+01 -1.0 4.18e+01 - 1.55e-01 1.62e-02h 1
4 2.4426939e+04 1.55e+01 3.37e+01 -1.0 4.49e+01 - 5.05e-01 8.84e-02h 1
5 3.5654049e+04 1.29e+01 3.04e+01 -1.0 7.04e+01 - 2.66e-01 1.69e-01H 1
6 4.5981864e+04 1.06e+01 3.76e+01 -1.0 1.07e+02 - 7.81e-01 1.83e-01h 1
7 6.4990845e+04 6.28e+00 2.46e+01 -1.0 1.29e+02 - 7.62e-01 4.05e-01H 1
8 7.2941782e+04 4.45e+00 1.98e+01 -1.0 1.46e+02 - 6.50e-01 2.92e-01h 1
9 7.8657212e+04 3.11e+00 1.34e+01 -1.0 1.50e+02 - 2.32e-01 3.01e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.2721881e+04 2.11e+00 8.87e+00 -1.0 1.56e+02 - 2.37e-01 3.22e-01h 1
11 8.6494258e+04 1.31e+00 8.94e+00 -1.0 1.47e+02 - 8.66e-01 3.79e-01h 1
12 9.1978730e+04 1.81e-01 3.49e+00 -1.0 1.42e+02 - 6.80e-01 8.76e-01h 1
13 9.2499709e+04 1.79e-01 6.09e+00 -1.0 1.18e+02 - 8.21e-01 6.38e-01h 1
14 9.2809697e+04 7.56e-03 5.99e-01 -1.0 8.99e+01 - 1.00e+00 1.00e+00H 1
15 9.2748777e+04 2.86e-03 2.52e+00 -1.7 2.65e+01 - 9.39e-01 7.19e-01h 1
16 9.2718202e+04 4.67e-03 8.70e-02 -1.7 1.35e+01 - 1.00e+00 1.00e+00h 1
17 9.2701386e+04 3.06e-03 1.06e+00 -2.5 1.16e+01 - 8.99e-01 7.12e-01h 1
18 9.2698201e+04 1.55e-03 2.09e+00 -2.5 5.16e+00 - 1.00e+00 5.76e-01h 1
19 9.2696531e+04 5.10e-04 3.57e-03 -2.5 2.53e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 9.2694394e+04 2.74e-04 1.07e+00 -3.8 2.72e+00 - 9.84e-01 7.42e-01h 1
21 9.2693778e+04 1.15e-04 6.40e-04 -3.8 1.13e+00 - 1.00e+00 1.00e+00h 1
22 9.2693813e+04 4.96e-06 2.97e-05 -3.8 9.20e-02 - 1.00e+00 1.00e+00h 1
23 9.2693672e+04 7.42e-06 1.07e-02 -5.7 2.34e-01 - 9.98e-01 9.80e-01h 1
24 9.2693671e+04 8.19e-07 9.01e-06 -5.7 1.39e-02 - 1.00e+00 1.00e+00h 1
25 9.2693670e+04 1.48e-07 8.95e-04 -8.6 3.09e-03 - 1.00e+00 9.48e-01h 1
26 9.2693670e+04 5.13e-09 5.45e-08 -8.6 3.72e-04 - 1.00e+00 1.00e+00f 1
27 9.2693670e+04 6.00e-10 3.72e-11 -8.6 2.54e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 27
(scaled) (unscaled)
Objective...............: 3.0897889867007520e+03 9.2693669601022557e+04
Dual infeasibility......: 3.7191041282897194e-11 1.1157312384869158e-09
Constraint violation....: 6.0031624116163584e-10 6.0031624116163584e-10
Variable bound violation: 7.6218864109023343e-08 7.6218864109023343e-08
Complementarity.........: 2.5423138023180662e-09 7.6269414069541986e-08
Overall NLP error.......: 2.5423138023180662e-09 7.6269414069541986e-08
Number of objective function evaluations = 35
Number of objective gradient evaluations = 28
Number of equality constraint evaluations = 35
Number of inequality constraint evaluations = 35
Number of equality constraint Jacobian evaluations = 28
Number of inequality constraint Jacobian evaluations = 28
Number of Lagrangian Hessian evaluations = 27
Total seconds in IPOPT = 715.313
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 2170
Number of nonzeros in inequality constraint Jacobian.: 704
Number of nonzeros in Lagrangian Hessian.............: 3460
Total number of variables............................: 517
variables with only lower bounds: 0
variables with lower and upper bounds: 457
variables with only upper bounds: 0
Total number of equality constraints.................: 473
Total number of inequality constraints...............: 352
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 352
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.8499996e+03 2.00e+01 1.41e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.3245588e+04 1.86e+01 3.73e+01 -1.0 7.57e+00 - 2.13e-03 7.03e-02h 3
2 1.7309771e+04 1.73e+01 3.47e+01 -1.0 1.53e+01 - 3.49e-02 6.78e-02h 1
3 1.8342944e+04 1.71e+01 3.43e+01 -1.0 4.18e+01 - 1.55e-01 1.62e-02h 1
4 2.4426939e+04 1.55e+01 3.37e+01 -1.0 4.49e+01 - 5.05e-01 8.84e-02h 1
5 3.5654049e+04 1.29e+01 3.04e+01 -1.0 7.04e+01 - 2.66e-01 1.69e-01H 1
6 4.5981864e+04 1.06e+01 3.76e+01 -1.0 1.07e+02 - 7.81e-01 1.83e-01h 1
7 6.4990845e+04 6.28e+00 2.46e+01 -1.0 1.29e+02 - 7.62e-01 4.05e-01H 1
8 7.2941782e+04 4.45e+00 1.98e+01 -1.0 1.46e+02 - 6.50e-01 2.92e-01h 1
9 7.8657212e+04 3.11e+00 1.34e+01 -1.0 1.50e+02 - 2.32e-01 3.01e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 8.2721881e+04 2.11e+00 8.87e+00 -1.0 1.56e+02 - 2.37e-01 3.22e-01h 1
11 8.6494258e+04 1.31e+00 8.94e+00 -1.0 1.47e+02 - 8.66e-01 3.79e-01h 1
12 9.1978730e+04 1.81e-01 3.49e+00 -1.0 1.42e+02 - 6.80e-01 8.76e-01h 1
13 9.2499709e+04 1.79e-01 6.09e+00 -1.0 1.18e+02 - 8.21e-01 6.38e-01h 1
14 9.2809697e+04 7.56e-03 5.99e-01 -1.0 8.99e+01 - 1.00e+00 1.00e+00H 1
15 9.2748777e+04 2.86e-03 2.52e+00 -1.7 2.65e+01 - 9.39e-01 7.19e-01h 1
16 9.2718202e+04 4.67e-03 8.70e-02 -1.7 1.35e+01 - 1.00e+00 1.00e+00h 1
17 9.2701386e+04 3.06e-03 1.06e+00 -2.5 1.16e+01 - 8.99e-01 7.12e-01h 1
18 9.2698201e+04 1.55e-03 2.09e+00 -2.5 5.16e+00 - 1.00e+00 5.76e-01h 1
19 9.2696531e+04 5.10e-04 3.57e-03 -2.5 2.53e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 9.2694394e+04 2.74e-04 1.07e+00 -3.8 2.72e+00 - 9.84e-01 7.42e-01h 1
21 9.2693778e+04 1.15e-04 6.40e-04 -3.8 1.13e+00 - 1.00e+00 1.00e+00h 1
22 9.2693813e+04 4.96e-06 2.97e-05 -3.8 9.20e-02 - 1.00e+00 1.00e+00h 1
23 9.2693672e+04 7.42e-06 1.07e-02 -5.7 2.34e-01 - 9.98e-01 9.80e-01h 1
24 9.2693671e+04 8.19e-07 9.01e-06 -5.7 1.39e-02 - 1.00e+00 1.00e+00h 1
25 9.2693670e+04 1.48e-07 8.95e-04 -8.6 3.09e-03 - 1.00e+00 9.48e-01h 1
26 9.2693670e+04 5.13e-09 5.45e-08 -8.6 3.72e-04 - 1.00e+00 1.00e+00f 1
27 9.2693670e+04 6.00e-10 3.72e-11 -8.6 2.54e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 27
(scaled) (unscaled)
Objective...............: 3.0897889867007520e+03 9.2693669601022557e+04
Dual infeasibility......: 3.7191041282897194e-11 1.1157312384869158e-09
Constraint violation....: 6.0031624116163584e-10 6.0031624116163584e-10
Variable bound violation: 7.6218864109023343e-08 7.6218864109023343e-08
Complementarity.........: 2.5423138023180662e-09 7.6269414069541986e-08
Overall NLP error.......: 2.5423138023180662e-09 7.6269414069541986e-08
Number of objective function evaluations = 35
Number of objective gradient evaluations = 28
Number of equality constraint evaluations = 35
Number of inequality constraint evaluations = 35
Number of equality constraint Jacobian evaluations = 28
Number of inequality constraint Jacobian evaluations = 28
Number of Lagrangian Hessian evaluations = 27
Total seconds in IPOPT = 0.174
EXIT: Optimal Solution Found.
file = "/tmp/jl_3GWXAw/pglib_opf_case6468_rte.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case6470_rte.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case6495_rte.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case6515_rte.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case7336_epigrids.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case73_ieee_rts.m"
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 3079
Number of nonzeros in inequality constraint Jacobian.: 960
Number of nonzeros in Lagrangian Hessian.............: 4867
Total number of variables............................: 821
variables with only lower bounds: 0
variables with lower and upper bounds: 748
variables with only upper bounds: 0
Total number of equality constraints.................: 627
Total number of inequality constraints...............: 480
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 480
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.2029395e+05 2.52e+00 4.58e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.6840177e+05 1.57e+00 6.28e+01 -1.0 2.15e+00 - 9.96e-03 3.79e-01h 1
2 1.5863973e+05 1.38e+00 5.55e+01 -1.0 5.37e+00 - 1.33e-02 1.21e-01f 1
3 1.5548642e+05 1.36e+00 5.17e+01 -1.0 7.22e+00 - 3.52e-01 1.30e-02f 1
4 1.5695662e+05 1.11e+00 4.06e+01 -1.0 6.22e+00 - 7.22e-01 1.83e-01h 1
5 1.5887394e+05 6.95e-01 2.54e+01 -1.0 7.76e+00 - 3.89e-01 3.74e-01h 1
6 1.5957285e+05 4.99e-01 2.10e+01 -1.0 9.38e+00 - 1.00e+00 2.82e-01h 1
7 1.6281289e+05 3.69e-01 1.55e+01 -1.0 6.50e+00 - 3.44e-01 2.60e-01h 1
8 1.6421420e+05 3.25e-01 1.22e+01 -1.0 5.49e+00 - 1.00e+00 1.19e-01h 1
9 1.6632915e+05 2.66e-01 7.11e+00 -1.0 3.63e+00 - 5.01e-01 1.83e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7504296e+05 1.51e-01 1.06e+01 -1.0 2.44e+00 - 6.58e-02 4.32e-01h 1
11 1.8861399e+05 6.24e-02 1.08e+01 -1.0 2.53e+00 - 2.74e-01 8.66e-01h 1
12 1.9092060e+05 2.17e-02 3.46e+00 -1.0 1.11e+00 - 7.53e-01 1.00e+00h 1
13 1.9108513e+05 3.84e-03 3.77e-01 -1.0 8.49e-01 - 1.00e+00 1.00e+00h 1
14 1.9022312e+05 1.55e-03 4.99e-01 -1.7 2.53e-01 - 8.65e-01 1.00e+00f 1
15 1.9008605e+05 1.06e-03 1.15e-02 -1.7 1.07e-01 - 1.00e+00 1.00e+00f 1
16 1.8987249e+05 8.00e-04 3.18e-01 -3.8 1.55e-01 - 8.03e-01 6.68e-01f 1
17 1.8982422e+05 5.39e-04 1.68e+00 -3.8 1.76e-01 - 8.21e-01 3.99e-01f 1
18 1.8979502e+05 3.18e-04 2.71e+00 -3.8 2.56e-01 - 8.80e-01 4.71e-01h 1
19 1.8976750e+05 9.31e-05 3.39e-01 -3.8 1.97e-01 - 1.00e+00 9.20e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.8976634e+05 3.16e-06 4.97e-05 -3.8 1.13e-02 - 1.00e+00 1.00e+00h 1
21 1.8976428e+05 8.44e-07 4.71e-02 -5.7 1.03e-02 - 9.81e-01 9.21e-01h 1
22 1.8976410e+05 4.22e-08 9.88e-07 -5.7 9.91e-04 - 1.00e+00 1.00e+00h 1
23 1.8976408e+05 3.98e-10 4.49e-07 -8.6 1.35e-04 - 1.00e+00 1.00e+00h 1
24 1.8976408e+05 3.32e-14 1.47e-12 -8.6 6.22e-05 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 24
(scaled) (unscaled)
Objective...............: 1.4597236705182579e+03 1.8976407716737353e+05
Dual infeasibility......: 1.4733067281826849e-12 1.9152987466374903e-10
Constraint violation....: 2.9872979090406202e-14 3.3192198989340227e-14
Variable bound violation: 3.9923579997491743e-08 3.9923579997491743e-08
Complementarity.........: 2.5067203665822374e-09 3.2587364765569084e-07
Overall NLP error.......: 2.5067203665822374e-09 3.2587364765569084e-07
Number of objective function evaluations = 25
Number of objective gradient evaluations = 25
Number of equality constraint evaluations = 25
Number of inequality constraint evaluations = 25
Number of equality constraint Jacobian evaluations = 25
Number of inequality constraint Jacobian evaluations = 25
Number of Lagrangian Hessian evaluations = 24
Total seconds in IPOPT = 1194.567
EXIT: Optimal Solution Found.
This is Ipopt version 3.14.19, running with linear solver MUMPS 5.9.0.
Number of nonzeros in equality constraint Jacobian...: 3079
Number of nonzeros in inequality constraint Jacobian.: 960
Number of nonzeros in Lagrangian Hessian.............: 4867
Total number of variables............................: 821
variables with only lower bounds: 0
variables with lower and upper bounds: 748
variables with only upper bounds: 0
Total number of equality constraints.................: 627
Total number of inequality constraints...............: 480
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 480
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.2029395e+05 2.52e+00 4.58e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.6840177e+05 1.57e+00 6.28e+01 -1.0 2.15e+00 - 9.96e-03 3.79e-01h 1
2 1.5863973e+05 1.38e+00 5.55e+01 -1.0 5.37e+00 - 1.33e-02 1.21e-01f 1
3 1.5548642e+05 1.36e+00 5.17e+01 -1.0 7.22e+00 - 3.52e-01 1.30e-02f 1
4 1.5695662e+05 1.11e+00 4.06e+01 -1.0 6.22e+00 - 7.22e-01 1.83e-01h 1
5 1.5887394e+05 6.95e-01 2.54e+01 -1.0 7.76e+00 - 3.89e-01 3.74e-01h 1
6 1.5957285e+05 4.99e-01 2.10e+01 -1.0 9.38e+00 - 1.00e+00 2.82e-01h 1
7 1.6281289e+05 3.69e-01 1.55e+01 -1.0 6.50e+00 - 3.44e-01 2.60e-01h 1
8 1.6421420e+05 3.25e-01 1.22e+01 -1.0 5.49e+00 - 1.00e+00 1.19e-01h 1
9 1.6632915e+05 2.66e-01 7.11e+00 -1.0 3.63e+00 - 5.01e-01 1.83e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.7504296e+05 1.51e-01 1.06e+01 -1.0 2.44e+00 - 6.58e-02 4.32e-01h 1
11 1.8861399e+05 6.24e-02 1.08e+01 -1.0 2.53e+00 - 2.74e-01 8.66e-01h 1
12 1.9092060e+05 2.17e-02 3.46e+00 -1.0 1.11e+00 - 7.53e-01 1.00e+00h 1
13 1.9108513e+05 3.84e-03 3.77e-01 -1.0 8.49e-01 - 1.00e+00 1.00e+00h 1
14 1.9022312e+05 1.55e-03 4.99e-01 -1.7 2.53e-01 - 8.65e-01 1.00e+00f 1
15 1.9008605e+05 1.06e-03 1.15e-02 -1.7 1.07e-01 - 1.00e+00 1.00e+00f 1
16 1.8987249e+05 8.00e-04 3.18e-01 -3.8 1.55e-01 - 8.03e-01 6.68e-01f 1
17 1.8982422e+05 5.39e-04 1.68e+00 -3.8 1.76e-01 - 8.21e-01 3.99e-01f 1
18 1.8979502e+05 3.18e-04 2.71e+00 -3.8 2.56e-01 - 8.80e-01 4.71e-01h 1
19 1.8976750e+05 9.31e-05 3.39e-01 -3.8 1.97e-01 - 1.00e+00 9.20e-01h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
20 1.8976634e+05 3.16e-06 4.97e-05 -3.8 1.13e-02 - 1.00e+00 1.00e+00h 1
21 1.8976428e+05 8.44e-07 4.71e-02 -5.7 1.03e-02 - 9.81e-01 9.21e-01h 1
22 1.8976410e+05 4.22e-08 9.88e-07 -5.7 9.91e-04 - 1.00e+00 1.00e+00h 1
23 1.8976408e+05 3.98e-10 4.49e-07 -8.6 1.35e-04 - 1.00e+00 1.00e+00h 1
24 1.8976408e+05 3.32e-14 1.47e-12 -8.6 6.22e-05 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 24
(scaled) (unscaled)
Objective...............: 1.4597236705182579e+03 1.8976407716737353e+05
Dual infeasibility......: 1.4733067281826849e-12 1.9152987466374903e-10
Constraint violation....: 2.9872979090406202e-14 3.3192198989340227e-14
Variable bound violation: 3.9923579997491743e-08 3.9923579997491743e-08
Complementarity.........: 2.5067203665822374e-09 3.2587364765569084e-07
Overall NLP error.......: 2.5067203665822374e-09 3.2587364765569084e-07
Number of objective function evaluations = 25
Number of objective gradient evaluations = 25
Number of equality constraint evaluations = 25
Number of inequality constraint evaluations = 25
Number of equality constraint Jacobian evaluations = 25
Number of inequality constraint Jacobian evaluations = 25
Number of Lagrangian Hessian evaluations = 24
Total seconds in IPOPT = 0.288
EXIT: Optimal Solution Found.
file = "/tmp/jl_3GWXAw/pglib_opf_case78484_epigrids.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case793_goc.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case8387_pegase.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case89_pegase.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case9241_pegase.m"
file = "/tmp/jl_3GWXAw/pglib_opf_case9591_goc.m"
10×23 DataFrame
Row │ case vars cons optimization optimization_m ⋯
│ String Int64 Int64 Float64 Float64 ⋯
─────┼──────────────────────────────────────────────────────────────────────────
1 │ pglib_opf_case14_ieee.m 118 169 2.45458 0. ⋯
2 │ pglib_opf_case24_ieee_rts.m 266 315 6.68505 0.
3 │ pglib_opf_case30_as.m 236 348 5.04651 0.
4 │ pglib_opf_case30_ieee.m 236 348 8.46107 0.
5 │ pglib_opf_case39_epri.m 282 401 11.4254 0. ⋯
6 │ pglib_opf_case3_lmbd.m 24 28 0.228645 0.
7 │ pglib_opf_case57_ieee.m 448 675 14.5332 0.
8 │ pglib_opf_case5_pjm.m 44 53 0.854507 0.
9 │ pglib_opf_case60_c.m 518 737 15.7333 0. ⋯
10 │ pglib_opf_case73_ieee_rts.m 824 987 13.6797 0.
19 columns omittedio = IOBuffer()
println(io, "```@raw html")
pretty_table(io, timing_data; backend = :html)
# show(io, "text/html", pretty_table(timing_data; backend = :html))
println(io, "```")
Text(String(take!(io)))| case | vars | cons | optimization | optimization_modelbuild | optimization_wcompilation | optimization_cost | mtk | mtk_time_modelbuild | mtk_time_wcompilation | mtk_cost | jump | jump_modelbuild | jump_wcompilation | jump_cost | nlpmodels | nlpmodels_modelbuild | nlpmodels_wcompilation | nlpmodels_cost | optim | optim_modelbuild | optim_wcompilation | optim_cost |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| String | Int64 | Int64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 |
| pglib_opf_case14_ieee.m | 118 | 169 | 2.45458 | 0.000281848 | 2.38896 | 2178.08 | 0.21121 | 1.15172 | 15.8506 | 2178.08 | 0.018768 | 0.366525 | 0.359803 | 2178.08 | 0.112615 | 0.116976 | 0.105085 | 2178.08 | 117.887 | 0.10918 | 118.243 | 1678.58 |
| pglib_opf_case24_ieee_rts.m | 266 | 315 | 6.68505 | 0.000356688 | 7.06892 | 63352.2 | 0.216824 | 3.12687 | 69.209 | 63352.2 | 0.0364183 | 0.00628918 | 0.0379707 | 63352.2 | 0.307031 | 0.025349 | 0.402922 | 63352.2 | 273.716 | 0.00251542 | 273.574 | 52954.1 |
| pglib_opf_case30_as.m | 236 | 348 | 5.04651 | 0.000421428 | 5.40984 | 803.127 | 0.44024 | 4.77129 | 95.3473 | 803.127 | 0.029549 | 0.0110926 | 0.0318049 | 803.127 | 0.187083 | 0.0368243 | 0.19091 | 803.127 | 214.84 | 0.00373024 | 216.152 | 751.912 |
| pglib_opf_case30_ieee.m | 236 | 348 | 8.46107 | 0.000541947 | 8.28266 | 8208.52 | 0.446932 | 2.75631 | 94.8594 | 8208.52 | 0.0355397 | 0.00659812 | 0.0375584 | 8208.52 | 0.4182 | 0.0261503 | 0.257021 | 8208.52 | 223.166 | 0.00256886 | 223.653 | 4176.94 |
| pglib_opf_case39_epri.m | 282 | 401 | 11.4254 | 0.000741305 | 11.5374 | 1.38416e5 | 0.531149 | 8.95899 | 136.173 | 1.38416e5 | 0.0606446 | 0.00763029 | 0.0611031 | 1.38416e5 | 0.544048 | 0.030644 | 0.384359 | 1.38416e5 | 361.615 | 0.00240675 | 365.475 | 66167.6 |
| pglib_opf_case3_lmbd.m | 24 | 28 | 0.228645 | 0.000420627 | 0.22658 | 5812.64 | 0.0294599 | 0.0984284 | 0.0298745 | 5812.64 | 0.0099276 | 0.0081074 | 0.0112228 | 5812.64 | 0.0319618 | 0.00777174 | 0.0290948 | 5812.64 | 0.509969 | 0.00126595 | 0.709406 | 1.93871e5 |
| pglib_opf_case57_ieee.m | 448 | 675 | 14.5332 | 0.000471187 | 14.3833 | 37589.3 | 0.895462 | 33.0129 | 542.746 | 37589.3 | 0.0582366 | 0.0178887 | 0.0704561 | 37589.3 | 0.639283 | 0.0631642 | 0.445055 | 37589.3 | NaN | NaN | NaN | NaN |
| pglib_opf_case5_pjm.m | 44 | 53 | 0.854507 | 0.000247408 | 0.71495 | 17551.9 | 0.026184 | 0.146326 | 0.0412655 | 17551.9 | 0.0134011 | 0.00355177 | 0.0142558 | 17551.9 | 0.0475236 | 0.00724887 | 0.0625621 | 17551.9 | 13.4214 | 0.00118493 | 13.6729 | 90.6969 |
| pglib_opf_case60_c.m | 518 | 737 | 15.7333 | 0.000929014 | 15.4897 | 92718.2 | 1.14394 | 41.7418 | 717.683 | 92693.7 | 0.0974842 | 0.0229023 | 0.0996278 | 92693.7 | 1.11326 | 0.0568903 | 1.25174 | 92693.7 | NaN | NaN | NaN | NaN |
| pglib_opf_case73_ieee_rts.m | 824 | 987 | 13.6797 | 0.000755224 | 13.6153 | 1.75043e5 | 1.29954 | 1507.88 | 1200.42 | 1.89764e5 | 0.136446 | 0.0283401 | 0.141398 | 1.89764e5 | 9.86316 | 0.100453 | 1.2991 | 1.89764e5 | NaN | NaN | NaN | NaN |
Appendix
Appendix
These benchmarks are a part of the SciMLBenchmarks.jl repository, found at: https://github.com/SciML/SciMLBenchmarks.jl. For more information on high-performance scientific machine learning, check out the SciML Open Source Software Organization https://sciml.ai.
To locally run this benchmark, do the following commands:
using SciMLBenchmarks
SciMLBenchmarks.weave_file("benchmarks/OptimizationFrameworks","optimal_powerflow.jmd")Computer Information:
Julia Version 1.11.9
Commit 53a02c0720c (2026-02-06 00:27 UTC)
Build Info:
Official https://julialang.org/ release
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 128 × AMD EPYC 7502 32-Core Processor
WORD_SIZE: 64
LLVM: libLLVM-16.0.6 (ORCJIT, znver2)
Threads: 128 default, 0 interactive, 64 GC (on 128 virtual cores)
Environment:
JULIA_NUM_THREADS = auto
Package Information:
Status `/julia/github-runners/amdci1-1/_work/SciMLBenchmarks.jl/SciMLBenchmarks.jl/benchmarks/OptimizationFrameworks/Project.toml`
[54578032] ADNLPModels v0.8.13
[6e4b80f9] BenchmarkTools v1.8.0
[2569d6c7] ConcreteStructs v0.2.8
⌃ [992eb4ea] CondaPkg v0.2.33
[a93c6f00] DataFrames v1.8.2
⌃ [7da242da] Enzyme v0.13.203
[f6369f11] ForwardDiff v1.4.6
[b6b21f68] Ipopt v1.16.0
[4076af6c] JuMP v1.31.2
[961ee093] ModelingToolkit v11.43.1
[f4238b75] NLPModelsIpopt v0.11.3
⌅ [429524aa] Optim v1.13.3
[7f7a1694] Optimization v5.9.1
[bca83a33] OptimizationBase v5.6.1
[fd9f6733] OptimizationMOI v1.4.1
[91a5bcdd] Plots v1.41.7
⌃ [c36e90e8] PowerModels v0.21.5
[08abe8d2] PrettyTables v3.4.8
[6099a3de] PythonCall v0.9.35
[37e2e3b7] ReverseDiff v1.17.0
[31c91b34] SciMLBenchmarks v0.2.1
[860ef19b] StableRNGs v1.0.4
[2efcf032] SymbolicIndexingInterface v0.3.55
[0c5d862f] Symbolics v7.39.2
[76f85450] LibGit2 v1.11.0
[8dfed614] Test v1.11.0
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated`And the full manifest:
Status `/julia/github-runners/amdci1-1/_work/SciMLBenchmarks.jl/SciMLBenchmarks.jl/benchmarks/OptimizationFrameworks/Manifest.toml`
[54578032] ADNLPModels v0.8.13
[47edcb42] ADTypes v1.24.0
[14f7f29c] AMD v0.5.4
[6e696c72] AbstractPlutoDingetjes v1.4.1
[1520ce14] AbstractTrees v0.4.5
[7d9f7c33] Accessors v0.1.45
[79e6a3ab] Adapt v4.7.0
[66dad0bd] AliasTables v1.1.3
[ec485272] ArnoldiMethod v0.4.0
⌃ [4fba245c] ArrayInterface v7.30.1
[4c555306] ArrayLayouts v1.12.2
[aae01518] BandedMatrices v1.12.0
[6e4b80f9] BenchmarkTools v1.8.0
[e2ed5e7c] Bijections v0.2.2
[b2a6c25c] BinaryHeaps v1.1.0
[caf10ac8] BipartiteGraphs v0.1.14
[8e7c35d0] BlockArrays v1.10.0
[70df07ce] BracketingNonlinearSolve v1.12.7
[fa961155] CEnum v0.5.0
[d360d2e6] ChainRulesCore v1.26.1
[523fee87] CodecBzip2 v0.8.5
[944b1d66] CodecZlib v0.7.9
[35d6a980] ColorSchemes v3.31.0
[3da002f7] ColorTypes v0.12.1
[c3611d14] ColorVectorSpace v0.11.0
[5ae59095] Colors v0.13.1
⌅ [861a8166] Combinatorics v1.0.2
[38540f10] CommonSolve v0.2.14
[bbf7d656] CommonSubexpressions v0.3.1
[f70d9fcc] CommonWorldInvalidations v1.2.2
[34da2185] Compat v4.18.1
[b152e2b5] CompositeTypes v0.1.4
[a33af91c] CompositionsBase v0.1.2
[2569d6c7] ConcreteStructs v0.2.8
⌃ [992eb4ea] CondaPkg v0.2.33
[88cd18e8] ConsoleProgressMonitor v0.1.2
[187b0558] ConstructionBase v1.6.0
[d38c429a] Contour v0.6.3
[a8cc5b0e] Crayons v4.2.0
[9a962f9c] DataAPI v1.16.0
[a93c6f00] DataFrames v1.8.2
[864edb3b] DataStructures v0.19.6
[e2d170a0] DataValueInterfaces v1.0.0
[8bb1440f] DelimitedFiles v1.9.1
[2b5f629d] DiffEqBase v7.21.1
[459566f4] DiffEqCallbacks v4.19.4
[163ba53b] DiffResults v1.1.0
[b552c78f] DiffRules v1.16.0
[a0c0ee7d] DifferentiationInterface v0.7.21
[b4f34e82] Distances v0.10.12
[ffbed154] DocStringExtensions v0.9.5
[5b8099bc] DomainSets v0.8.1
[7c1d4256] DynamicPolynomials v0.6.8
[4e289a0a] EnumX v1.0.7
⌃ [7da242da] Enzyme v0.13.203
[f151be2c] EnzymeCore v0.8.21
[e2ba6199] ExprTools v0.1.11
[55351af7] ExproniconLite v0.10.14
[c87230d0] FFMPEG v0.4.5
[7034ab61] FastBroadcast v1.4.0
[9aa1b823] FastClosures v0.3.2
[a4df4552] FastPower v1.5.0
[1a297f60] FillArrays v1.17.0
[64ca27bc] FindFirstFunctions v3.2.1
[6a86dc24] FiniteDiff v2.33.0
⌅ [53c48c17] FixedPointNumbers v0.8.6
[1fa38f19] Format v1.3.7
[f6369f11] ForwardDiff v1.4.6
[a85aefff] FunctionMaps v0.1.2
[069b7b12] FunctionWrappers v1.1.3
[77dc65aa] FunctionWrappersWrappers v1.13.0
[46192b85] GPUArraysCore v0.2.0
⌅ [61eb1bfa] GPUCompiler v1.23.0
[28b8d3ca] GR v0.73.27
[86223c79] Graphs v1.15.0
[076d061b] HashArrayMappedTries v0.2.0
⌅ [eafb193a] Highlights v0.5.3
[3263718b] ImplicitDiscreteSolve v2.3.0
[d25df0c9] Inflate v0.1.5
[2030c09a] InfrastructureModels v0.7.9
⌅ [842dd82b] InlineStrings v1.4.6
[18e54dd8] IntegerMathUtils v0.1.4
[8197267c] IntervalSets v0.7.14
[3587e190] InverseFunctions v0.1.17
[41ab1584] InvertedIndices v1.3.1
[b6b21f68] Ipopt v1.16.0
[92d709cd] IrrationalConstants v0.2.6
[82899510] IteratorInterfaceExtensions v1.0.0
[1019f520] JLFzf v0.1.11
[692b3bcd] JLLWrappers v1.8.0
⌅ [682c06a0] JSON v0.21.4
[0f8b85d8] JSON3 v1.14.3
[ae98c720] Jieko v0.2.1
[4076af6c] JuMP v1.31.2
⌃ [ccbc3e58] JumpProcesses v9.32.3
[ba0b0d4f] Krylov v0.10.10
[2faa5264] LHLFactorization v2.2.2
[929cbde3] LLVM v9.13.1
[b964fa9f] LaTeXStrings v1.4.1
[23fbe1c1] Latexify v0.16.12
[1d6d02ad] LeftChildRightSiblingTrees v0.3.0
[87fe0de2] LineSearch v0.1.18
⌃ [d3d80556] LineSearches v7.5.1
[5c8ed15e] LinearOperators v2.14.2
⌃ [7ed4a6bd] LinearSolve v5.17.3
[2ab3a3ac] LogExpFunctions v1.0.1
[e6f89c97] LoggingExtras v1.2.0
[1914dd2f] MacroTools v0.5.16
[b8f27783] MathOptInterface v1.53.0
[bb5d69b7] MaybeInplace v0.1.8
[442fdcdd] Measures v0.3.3
[f28f55f0] Memento v1.5.0
[0b3b1443] MicroMamba v0.1.15
[e1d29d7a] Missings v1.2.0
[961ee093] ModelingToolkit v11.43.1
⌃ [7771a370] ModelingToolkitBase v1.70.0
[6bb917b9] ModelingToolkitTearing v1.20.6
⌅ [2e0e35c7] Moshi v0.3.9
[46d2c3a1] MuladdMacro v0.2.7
[102ac46a] MultivariatePolynomials v0.5.19
[ffc61752] Mustache v1.0.21
[d8a4904e] MutableArithmetics v1.8.0
[a4795742] NLPModels v0.21.12
[f4238b75] NLPModelsIpopt v0.11.3
[e01155f1] NLPModelsModifiers v0.8.0
⌅ [d41bc354] NLSolversBase v7.10.0
⌅ [2774e3e8] NLsolve v4.5.1
[77ba4419] NaNMath v1.1.4
⌃ [be0214bd] NonlinearSolveBase v2.48.0
⌃ [5959db7a] NonlinearSolveFirstOrder v2.6.1
[d8793406] ObjectFile v0.5.1
[6fe1bfb0] OffsetArrays v1.17.0
⌅ [429524aa] Optim v1.13.3
[7f7a1694] Optimization v5.9.1
[bca83a33] OptimizationBase v5.6.1
[fd9f6733] OptimizationMOI v1.4.1
[bac558e1] OrderedCollections v2.0.1
⌃ [bbf590c4] OrdinaryDiffEqCore v4.17.2
⌅ [69de0a69] Parsers v2.8.8
[fa939f87] Pidfile v1.3.0
[ccf2f8ad] PlotThemes v3.3.0
[995b91a9] PlotUtils v1.4.4
[91a5bcdd] Plots v1.41.7
[e409e4f3] PoissonRandom v0.4.13
[2dfb63ee] PooledArrays v1.4.3
[85a6dd25] PositiveFactorizations v0.2.4
⌃ [c36e90e8] PowerModels v0.21.5
[d236fae5] PreallocationTools v1.7.1
⌅ [aea7be01] PrecompileTools v1.2.1
[21216c6a] Preferences v1.6.0
[08abe8d2] PrettyTables v3.4.8
[27ebfcd6] Primes v0.5.7
[33c8b6b6] ProgressLogging v0.1.6
[92933f4c] ProgressMeter v1.11.0
[43287f4e] PtrArrays v1.4.0
[0c0d3e7f] PureKLU v1.5.0
[6099a3de] PythonCall v0.9.35
[988b38a3] ReadOnlyArrays v0.2.0
[795d4caa] ReadOnlyDicts v1.0.1
[3cdcf5f2] RecipesBase v1.3.4
[01d81517] RecipesPipeline v0.6.12
[731186ca] RecursiveArrayTools v4.5.1
[189a3867] Reexport v1.2.2
[05181044] RelocatableFolders v1.0.1
[ae029012] Requires v1.3.1
[9fe22ead] RespecializeParams v1.3.0
[37e2e3b7] ReverseDiff v1.17.0
[7e49a35a] RuntimeGeneratedFunctions v0.5.26
[9dfe8606] SCCNonlinearSolve v1.15.3
⌃ [0bca4576] SciMLBase v3.54.0
[31c91b34] SciMLBenchmarks v0.2.1
[19f34311] SciMLJacobianOperators v0.1.19
[a6db7da4] SciMLLogging v2.1.0
⌃ [c0aeaf25] SciMLOperators v1.30.0
[431bcebd] SciMLPublic v1.3.0
[53ae85a6] SciMLStructures v1.10.5
[7e506255] ScopedValues v1.6.2
[6c6a2e73] Scratch v1.3.0
[91c51154] SentinelArrays v1.4.10
[efcf1570] Setfield v1.1.2
[992d4aef] Showoff v1.1.1
[727e6d20] SimpleNonlinearSolve v2.14.5
[699a6c99] SimpleTraits v0.9.6
[ff4d7338] SolverCore v0.3.10
[a2af1166] SortingAlgorithms v1.2.3
[a57abbd0] SparseColumnPivotedQR v2.1.8
[9f842d2f] SparseConnectivityTracer v1.2.3
[0a514795] SparseMatrixColorings v0.4.28
[276daf66] SpecialFunctions v2.9.0
[860ef19b] StableRNGs v1.0.4
[0c0c59c1] StarAlgebras v0.3.0
[64909d44] StateSelection v1.11.1
[90137ffa] StaticArrays v1.9.20
[1e83bf80] StaticArraysCore v1.4.4
[10745b16] Statistics v1.11.5
[82ae8749] StatsAPI v1.8.0
[2913bbd2] StatsBase v0.34.13
[69024149] StringEncodings v0.3.7
⌅ [892a3eda] StringManipulation v0.5.0
[53d494c1] StructIO v0.3.1
[856f2bd8] StructTypes v1.11.0
[2efcf032] SymbolicIndexingInterface v0.3.55
[19f23fe9] SymbolicLimits v1.2.1
[d1185830] SymbolicUtils v4.46.6
[0c5d862f] Symbolics v7.39.2
[3783bdb8] TableTraits v1.0.1
[bd369af6] Tables v1.14.0
[ed4db957] TaskLocalValues v0.1.3
[62fd8b95] TensorCore v0.1.1
[8ea1fca8] TermInterface v2.0.0
[5d786b92] TerminalLoggers v0.1.8
⌅ [a759f4b9] TimerOutputs v0.5.29
[e689c965] Tracy v0.1.6
[3bb67fe8] TranscodingStreams v0.11.3
[781d530d] TruncatedStacktraces v1.4.0
[3a884ed6] UnPack v1.0.2
[1cfade01] UnicodeFun v0.4.1
[e17b2a0c] UnsafePointers v1.0.0
[41fe7b60] Unzip v0.2.0
[d30d5f5c] WeakCacheSets v0.1.0
[44d3d7a6] Weave v0.10.12
[ddb6d928] YAML v0.4.16
[ae81ac8f] ASL_jll v0.1.5+0
[6e34b625] Bzip2_jll v1.0.9+0
[83423d85] Cairo_jll v1.18.7+0
[ee1fde0b] Dbus_jll v1.16.2+0
[7cc45869] Enzyme_jll v0.0.293+0
[2702e6a9] EpollShim_jll v0.0.20230411+1
[2e619515] Expat_jll v2.8.4+0
⌅ [b22a6f82] FFMPEG_jll v8.1.2+0
[a3f928ae] Fontconfig_jll v2.17.1+0
[d7e528f0] FreeType2_jll v2.14.3+1
[559328eb] FriBidi_jll v1.0.17+0
[0656b61e] GLFW_jll v3.5.1+0
[d2c73de3] GR_jll v0.73.27+0
⌅ [b0724c58] GettextRuntime_jll v0.22.4+0
[61579ee1] Ghostscript_jll v9.55.1+0
[7746bdde] Glib_jll v2.88.3+0
[3b182d85] Graphite2_jll v1.3.16+0
[2e76f6c2] HarfBuzz_jll v100.14004.0+0
[e33a78d0] Hwloc_jll v2.14.0+0
[1d5cc7b8] IntelOpenMP_jll v2025.2.0+0
⌅ [9cc047cb] Ipopt_jll v300.1400.1902+0
[aacddb02] JpegTurbo_jll v3.2.0+1
[c1c5ebd0] LAME_jll v3.100.3+0
[88015f11] LERC_jll v4.2.0+0
[dad2f222] LLVMExtra_jll v0.0.47+0
[1d63c593] LLVMOpenMP_jll v23.1.1+0
[ad6e5548] LibTracyClient_jll v0.13.1+0
⌅ [e9f186c6] Libffi_jll v3.4.7+0
[7e76a0d4] Libglvnd_jll v1.7.1+1
[94ce4f54] Libiconv_jll v1.18.0+0
[4b2f31a3] Libmount_jll v2.42.0+0
[89763e89] Libtiff_jll v4.7.3+0
[38a345b3] Libuuid_jll v2.42.0+0
[d00139f3] METIS_jll v5.1.4+0
[856f044c] MKL_jll v2025.2.0+0
[d7ed1dd3] MUMPS_seq_jll v500.900.100+0
[e7412a2a] Ogg_jll v1.3.6+0
[656ef2d0] OpenBLAS32_jll v0.3.34+0
[458c3c95] OpenSSL_jll v3.5.8+0
[efe28fd5] OpenSpecFun_jll v0.5.6+0
[91d4177d] Opus_jll v1.6.1+0
[36c8627f] Pango_jll v1.58.2+0
[30392449] Pixman_jll v0.46.4+0
[c0090381] Qt6Base_jll v6.10.2+2
[629bc702] Qt6Declarative_jll v6.10.2+2
[ce943373] Qt6ShaderTools_jll v6.10.2+1
[6de9746b] Qt6Svg_jll v6.10.2+0
[e99dba38] Qt6Wayland_jll v6.10.2+1
[319450e9] SPRAL_jll v2025.9.18+1
[a44049a8] Vulkan_Loader_jll v1.3.243+0
[a2964d1f] Wayland_jll v1.24.0+0
⌅ [02c8fc9c] XML2_jll v2.13.9+0
[ffd25f8a] XZ_jll v5.8.4+0
[f67eecfb] Xorg_libICE_jll v1.1.2+0
[c834827a] Xorg_libSM_jll v1.2.6+0
[4f6342f7] Xorg_libX11_jll v1.8.13+0
[0c0b7dd1] Xorg_libXau_jll v1.0.13+0
[935fb764] Xorg_libXcursor_jll v1.2.4+0
[a3789734] Xorg_libXdmcp_jll v1.1.6+0
[1082639a] Xorg_libXext_jll v1.3.8+0
[d091e8ba] Xorg_libXfixes_jll v6.0.2+0
[a51aa0fd] Xorg_libXi_jll v1.8.4+0
[d1454406] Xorg_libXinerama_jll v1.1.7+0
[ec84b674] Xorg_libXrandr_jll v1.5.6+0
[ea2f1a96] Xorg_libXrender_jll v0.9.12+0
[a65dc6b1] Xorg_libpciaccess_jll v0.19.0+0
[c7cfdc94] Xorg_libxcb_jll v1.17.1+0
[cc61e674] Xorg_libxkbfile_jll v1.2.0+0
[e920d4aa] Xorg_xcb_util_cursor_jll v0.1.6+0
[12413925] Xorg_xcb_util_image_jll v0.4.1+0
[2def613f] Xorg_xcb_util_jll v0.4.1+0
[975044d2] Xorg_xcb_util_keysyms_jll v0.4.1+0
[0d47668e] Xorg_xcb_util_renderutil_jll v0.3.10+0
[c22f9ab0] Xorg_xcb_util_wm_jll v0.4.2+0
[35661453] Xorg_xkbcomp_jll v1.4.7+0
[33bec58e] Xorg_xkeyboard_config_jll v2.47.0+2
[c5fb5394] Xorg_xtrans_jll v1.6.0+0
[3161d3a3] Zstd_jll v1.5.7+1
[35ca27e7] eudev_jll v3.2.14+0
⌅ [214eeab7] fzf_jll v0.61.1+0
[a4ae2306] libaom_jll v3.14.1+0
[0ac62f75] libass_jll v0.17.5+0
[1183f4f0] libdecor_jll v0.2.2+0
[8e53e030] libdrm_jll v2.4.134+0
[2db6ffa8] libevdev_jll v1.13.4+0
[f638f0a6] libfdk_aac_jll v2.0.4+0
[36db933b] libinput_jll v1.28.1+0
[b53b4c65] libpng_jll v1.6.58+0
[9a156e7d] libva_jll v2.23.0+0
[f27f6e37] libvorbis_jll v1.3.8+0
[f8abcde7] micromamba_jll v2.3.1+0
[009596ad] mtdev_jll v1.1.7+0
[1317d2d5] oneTBB_jll v2022.3.0+0
[4d7b5844] pixi_jll v0.76.2+0
⌅ [1270edf5] x264_jll v10164.0.1+0
[dfaa095f] x265_jll v4.1.0+0
[d8fb68d0] xkbcommon_jll v1.13.0+0
[0dad84c5] ArgTools v1.1.2
[56f22d72] Artifacts v1.11.0
[2a0f44e3] Base64 v1.11.0
[ade2ca70] Dates v1.11.0
[8ba89e20] Distributed v1.11.0
[f43a241f] Downloads v1.6.0
[7b1f6079] FileWatching v1.11.0
[9fa8497b] Future v1.11.0
[b77e0a4c] InteractiveUtils v1.11.0
[4af54fe1] LazyArtifacts v1.11.0
[b27032c2] LibCURL v0.6.4
[76f85450] LibGit2 v1.11.0
[8f399da3] Libdl v1.11.0
[37e2e46d] LinearAlgebra v1.11.0
[56ddb016] Logging v1.11.0
[d6f4376e] Markdown v1.11.0
[a63ad114] Mmap v1.11.0
[ca575930] NetworkOptions v1.2.0
[44cfe95a] Pkg v1.11.0
[de0858da] Printf v1.11.0
[9abbd945] Profile v1.11.0
[3fa0cd96] REPL v1.11.0
[9a3f8284] Random v1.11.0
[ea8e919c] SHA v0.7.0
[9e88b42a] Serialization v1.11.0
[6462fe0b] Sockets v1.11.0
[2f01184e] SparseArrays v1.11.0
[f489334b] StyledStrings v1.11.0
[fa267f1f] TOML v1.0.3
[a4e569a6] Tar v1.10.0
[8dfed614] Test v1.11.0
[cf7118a7] UUIDs v1.11.0
[4ec0a83e] Unicode v1.11.0
[e66e0078] CompilerSupportLibraries_jll v1.1.1+0
[deac9b47] LibCURL_jll v8.6.0+0
[e37daf67] LibGit2_jll v1.7.2+0
[29816b5a] LibSSH2_jll v1.11.0+1
[c8ffd9c3] MbedTLS_jll v2.28.6+0
[14a3606d] MozillaCACerts_jll v2023.12.12
[4536629a] OpenBLAS_jll v0.3.27+1
[05823500] OpenLibm_jll v0.8.5+0
[efcefdf7] PCRE2_jll v10.42.0+1
[bea87d4a] SuiteSparse_jll v7.7.0+0
[83775a58] Zlib_jll v1.2.13+1
[8e850b90] libblastrampoline_jll v5.11.0+0
[8e850ede] nghttp2_jll v1.59.0+0
[3f19e933] p7zip_jll v17.4.0+2
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`