Nonlinear Solver 23 Test Problems
These benchmarks compares the runtime and error for a range of nonlinear solvers. The problems are a standard set of problems as described here. The solvers are implemented in NonlinearProblemLibrary.jl, where you can find the problem function declarations. For each problem we test the following solvers:
- NonlinearSolve.jl's Newton Raphson method (
NewtonRaphson()). - NonlinearSolve.jl's Trust Region method (
TrustRegion()). - NonlinearSolve.jl's Levenberg-Marquardt method (
LevenbergMarquardt()). - MINPACK's Modified Powell method (
CMINPACK(method=:hybr)). - MINPACK's Levenberg-Marquardt method (
CMINPACK(method=:lm)). - NLsolveJL's Newton Raphson (
NLsolveJL(method=:newton)). - NLsolveJL's Trust Region (
NLsolveJL()). - NLsolveJL's Anderson acceleration (
NLsolveJL(method=:anderson)). - Sundials's Newton-Krylov method (
KINSOL()).
Furthermore, for NonlinearSolve.jl's Newton Raphson method we try the following Line Search options (in addition to the default):
HagerZhangMoreThuenteBackTracking
and for NonlinearSolve.jl's Trust Region we try the following Radius Update schemes (in addition to the default):
NLsolveNocedalWrightHeiYuanBastinFan
and finally for NonlinearSolve.jl's Levenberg-Marquardt method why try using both the default α_geodesic value (0.75) and a modified value (0.5), and also with and without setting the CholeskyFactorization linear solver.
For each benchmarked problem, the second, third, and fourth plots compares the performance of NonlinearSolve's Newton Raphson, Trust Region, and Levenberg-Marquardt methods, respectively. The first plot compares the best methods from each of these categories to the various methods available from other packages. At the end of the benchmarks, we print a summary table of which solvers succeeded for which problems.
Setup
Fetch required packages.
using NonlinearSolve, LinearSolve, StaticArrays, Sundials, Setfield,
BenchmarkTools, LinearAlgebra, DiffEqDevTools, NonlinearProblemLibrary, CairoMakie
import PolyesterForwardDiff, MINPACK, NLsolve
const RUS = RadiusUpdateSchemes;Declare the benchmarked solvers (and their names and plotting options).
# XXX: Add PETSc
solvers_all = [
(; pkg = :nonlinearsolve, type = :general, name = "Default PolyAlg.", solver = Dict(:alg => FastShortcutNonlinearPolyalg(; u0_len = 10))),
(; pkg = :nonlinearsolve, type = :NR, name = "Newton Raphson", solver = Dict(:alg => NewtonRaphson())),
(; pkg = :nonlinearsolve, type = :NR, name = "NR (HagerZhang)", solver = Dict(:alg => NewtonRaphson(; linesearch = HagerZhang()))),
(; pkg = :nonlinearsolve, type = :NR, name = "NR (MoreThuente)", solver = Dict(:alg => NewtonRaphson(; linesearch = MoreThuente()))),
(; pkg = :nonlinearsolve, type = :NR, name = "NR (BackTracking)", solver = Dict(:alg => NewtonRaphson(; linesearch = BackTracking()))),
(; pkg = :nonlinearsolve, type = :TR, name = "Trust Region", solver = Dict(:alg => TrustRegion())),
(; pkg = :nonlinearsolve, type = :TR, name = "TR (NLsolve Update)", solver = Dict(:alg => TrustRegion(; radius_update_scheme = RUS.NLsolve))),
(; pkg = :nonlinearsolve, type = :TR, name = "TR (Nocedal Wright)", solver = Dict(:alg => TrustRegion(; radius_update_scheme = RUS.NocedalWright))),
(; pkg = :nonlinearsolve, type = :TR, name = "TR (Hei)", solver = Dict(:alg => TrustRegion(; radius_update_scheme = RUS.Hei))),
(; pkg = :nonlinearsolve, type = :TR, name = "TR (Yuan)", solver = Dict(:alg => TrustRegion(; radius_update_scheme = RUS.Yuan))),
(; pkg = :nonlinearsolve, type = :TR, name = "TR (Bastin)", solver = Dict(:alg => TrustRegion(; radius_update_scheme = RUS.Bastin))),
(; pkg = :nonlinearsolve, type = :TR, name = "TR (Fan)", solver = Dict(:alg => TrustRegion(; radius_update_scheme = RUS.Fan))),
(; pkg = :nonlinearsolve, type = :LM, name = "Levenberg-Marquardt", solver = Dict(:alg => LevenbergMarquardt(; linsolve = QRFactorization()))),
(; pkg = :nonlinearsolve, type = :LM, name = "LM with Cholesky", solver = Dict(:alg => LevenbergMarquardt(; linsolve = CholeskyFactorization()))),
(; pkg = :nonlinearsolve, type = :LM, name = "LM (α_geodesic=0.5)", solver = Dict(:alg => LevenbergMarquardt(; linsolve = QRFactorization(), α_geodesic=0.5))),
(; pkg = :nonlinearsolve, type = :LM, name = "LM (α_geodesic=0.5) Chol.", solver = Dict(:alg => LevenbergMarquardt(; linsolve = CholeskyFactorization(), α_geodesic=0.5))),
(; pkg = :nonlinearsolve, type = :LM, name = "LM (no Accln.)", solver = Dict(:alg => LevenbergMarquardt(; linsolve = QRFactorization(), disable_geodesic = Val(true)))),
(; pkg = :nonlinearsolve, type = :LM, name = "LM (no Accln.) Chol.", solver = Dict(:alg => LevenbergMarquardt(; linsolve = CholeskyFactorization(), disable_geodesic = Val(true)))),
(; pkg = :nonlinearsolve, type = :general, name = "Pseudo Transient", solver = Dict(:alg => PseudoTransient(; alpha_initial=10.0))),
(; pkg = :wrapper, type = :general, name = "Powell [MINPACK]", solver = Dict(:alg => CMINPACK(; method=:hybr))),
(; pkg = :wrapper, type = :general, name = "LM [MINPACK]", solver = Dict(:alg => CMINPACK(; method=:lm))),
(; pkg = :wrapper, type = :general, name = "NR [NLsolve.jl]", solver = Dict(:alg => NLsolveJL(; method=:newton))),
(; pkg = :wrapper, type = :general, name = "TR [NLsolve.jl]", solver = Dict(:alg => NLsolveJL())),
(; pkg = :wrapper, type = :general, name = "NR [Sundials]", solver = Dict(:alg => KINSOL())),
(; pkg = :wrapper, type = :general, name = "NR LineSearch [Sundials]", solver = Dict(:alg => KINSOL(; globalization_strategy=:LineSearch)))
];
solver_tracker = [];
wp_general_tracker = [];Error: UndefVarError: `HagerZhang` not definedSets tolerances.
abstols = 1.0 ./ 10.0 .^ (4:12)
reltols = 1.0 ./ 10.0 .^ (4:12);Prepares various helper functions for benchmarking a specific problem.
# Benchmarks a specific problem, checks which solvers can solve it and their performance
function benchmark_problem!(prob_name; solver_tracker=solver_tracker)
# Finds the problem and the true solution.
prob = nlprob_23_testcases[prob_name]
# Finds the solvers that can solve the problem
successful_solvers = filter(Base.Fix1(check_solver, prob), solvers_all);
push!(solver_tracker, prob_name => successful_solvers);
# Handles the non-general cases.
solvers_NR = filter(s -> s.type==:NR, successful_solvers)
solvers_TR = filter(s -> s.type==:TR, successful_solvers)
solvers_LM = filter(s -> s.type==:LM, successful_solvers)
wp_NR = WorkPrecisionSet(prob.prob, abstols, reltols, getfield.(solvers_NR, :solver);
names=getfield.(solvers_NR, :name), numruns=100, error_estimate=:l∞,
maxiters=1000,
termination_condition = NonlinearSolve.AbsNormTerminationMode(Base.Fix1(maximum, abs)))
wp_TR = WorkPrecisionSet(prob.prob, abstols, reltols, getfield.(solvers_TR, :solver);
names=getfield.(solvers_TR, :name), numruns=100, error_estimate=:l∞,
maxiters=1000,
termination_condition = NonlinearSolve.AbsNormTerminationMode(Base.Fix1(maximum, abs)))
wp_LM = WorkPrecisionSet(prob.prob, abstols, reltols, getfield.(solvers_LM, :solver);
names=getfield.(solvers_LM, :name), numruns=100, error_estimate=:l∞,
maxiters=1000,
termination_condition = NonlinearSolve.AbsNormTerminationMode(Base.Fix1(maximum, abs)))
# Handles the general case
solvers_general = filter(s -> s.type==:general, successful_solvers)
add_solver!(solvers_general, nothing, solvers_TR, wp_TR)
add_solver!(solvers_general, nothing, solvers_LM, wp_LM)
add_solver!(solvers_general, nothing, solvers_NR, wp_NR)
wp_general = WorkPrecisionSet(prob.prob, abstols, reltols,
getfield.(solvers_general, :solver); names=getfield.(solvers_general, :name),
numruns=100, error_estimate=:l∞, maxiters=1000)
push!(wp_general_tracker, prob_name => wp_general)
fig = plot_collective_benchmark(prob_name, wp_general, wp_NR, wp_TR, wp_LM)
save(replace(lowercase(prob_name), " " => "_") * "_wpd.svg", fig)
return fig
end
# Checks if a solver can successfully solve a given problem.
function check_solver(prob, solver)
try
sol = solve(prob.prob, solver.solver[:alg]; abstol=1e-8, reltol=1e-8,
maxiters=1000000,
termination_condition=NonlinearSolve.AbsNormTerminationMode(Base.Fix1(maximum, abs)))
if norm(sol.resid, Inf) < 1e-6
Base.printstyled("[Info] Solver $(solver.name) returned retcode $(sol.retcode) \
with an residual norm = $(norm(sol.resid, Inf)).\n"; color=:green)
return true
else
Base.printstyled("[Warn] Solver $(solver.name) had a very large residual \
(norm = $(norm(sol.resid, Inf))).\n"; color=:red)
return false
end
WorkPrecisionSet(prob.prob, [1e-4, 1e-12], [1e-4, 1e-12], [solver.solver];
names=[solver.name], numruns=5, error_estimate=:l∞, maxiters=1000)
catch e
Base.printstyled("[Warn] Solver $(solver.name) threw an error: $e.\n"; color=:red)
return false
end
return true
end
# Adds an additional, selected, solver to the general solver set.
# Adds an additional, selected, solver to the general solver set.
function add_solver!(solvers_general, selected_solver_name, additional_solver_set, wp)
if isnothing(selected_solver_name)
isempty(wp.wps) && return
selected_idx = argmin(median.(getfield.(wp.wps, :times)))
else
selected_idx = findfirst(s -> s.name==selected_solver_name, additional_solver_set)
isnothing(selected_solver) && error("The $(selected_solver_name) was designated to \
be added to the general solver set, however, it seemed to fail on this \
problem.")
end
isnothing(selected_idx) ||
pushfirst!(solvers_general, additional_solver_set[selected_idx])
end;Plotting related helper functions.
__log10_zero(x) = ifelse(iszero(x), -100, log10(x))
Makie.inverse_transform(::typeof(__log10_zero)) = exp10
Makie.defaultlimits(::typeof(__log10_zero)) = Makie.defaultlimits(log10)
Makie.defined_interval(::typeof(__log10_zero)) = 0.0..Inf
# Skip minor ticks for __log10_zero scale
function Makie.get_minor_tickvalues(i::IntervalsBetween, scale::typeof(__log10_zero),
tickvalues, vmin, vmax)
return []
end
tickformatter(values) = map(values) do v
e = log10(v)
if isinteger(e) && e == -100
return rich("10", superscript("-∞"))
end
sup = isinteger(e) ? Int(e) : round(e; digits=2)
return rich("10", superscript(string(sup)))
end
function __filter_nearzero((ticks, ticklabels))
if first(ticks) ≈ 1e-100
idxs = findall(x -> x ≈ 1e-100 || x ≥ 10^-40, ticks)
return ticks[idxs], ticklabels[idxs]
end
return ticks, ticklabels
end
# Plots a work-precision diagram.
function plot_collective_benchmark(prob_name, wp_general, wp_NR, wp_TR, wp_LM)
LINESTYLES = Dict(:nonlinearsolve => :solid, :simplenonlinearsolve => :dash,
:wrapper => :dot)
ASPECT_RATIO = 0.7
WIDTH = 1400
HEIGHT = round(Int, WIDTH * ASPECT_RATIO)
STROKEWIDTH = 2.5
colors = cgrad(:seaborn_bright, length(solvers_all); categorical = true)
cycle = Cycle([:marker], covary = true)
plot_theme = Theme(Lines = (; cycle), Scatter = (; cycle))
fig = with_theme(plot_theme) do
fig = Figure(; size = (WIDTH, HEIGHT))
axs = []
xmin, xmax, ymin, ymax = Inf, -Inf, Inf, -Inf
for i in 1:2, j in 1:2
wp = (wp_general, wp_NR, wp_TR, wp_LM)[2 * (i - 1) + j]
ax = Axis(fig[i + 1, j], ylabel = j == 1 ? L"Time $\mathbf{(s)}$" : "",
xlabelsize = 22, ylabelsize = 22,
xlabel = i == 2 ? L"Error: $\mathbf{||f(u^\ast)||_\infty}$" : "",
xscale = __log10_zero, yscale = __log10_zero,
xtickwidth = STROKEWIDTH,
ytickwidth = STROKEWIDTH, spinewidth = STROKEWIDTH,
xticklabelsize = 20, yticklabelsize = 20,
xticklabelsvisible = i == 2, yticklabelsvisible = j == 1,
xticksvisible = i == 2, yticksvisible = j == 1,)
push!(axs, ax)
ls = []
scs = []
for wpᵢ in wp.wps
idx = findfirst(s -> s.name == wpᵢ.name, solvers_all)
errs = getindex.(wpᵢ.errors, :l∞)
times = wpᵢ.times
emin, emax = extrema(errs)
tmin, tmax = extrema(times)
emin < xmin && (xmin = emin)
emax > xmax && (xmax = emax)
tmin < ymin && (ymin = tmin)
tmax > ymax && (ymax = tmax)
l = lines!(ax, errs, times; color = colors[idx], linewidth = 5,
linestyle = LINESTYLES[solvers_all[idx].pkg], alpha = 0.8,
label = wpᵢ.name)
sc = scatter!(ax, errs, times; color = colors[idx], markersize = 16,
strokewidth = 2, marker = Cycled(idx), alpha = 0.8, label = wpᵢ.name)
push!(ls, l)
push!(scs, sc)
end
legend_title = ("", "Newton Raphson", "Trust Region", "Levenberg-Marquardt")[2 * (i - 1) + j]
Legend(fig[ifelse(i == 1, 1, 4), j], [[l, sc] for (l, sc) in zip(ls, scs)],
[wpᵢ.name for wpᵢ in wp.wps], legend_title;
framevisible=true, framewidth = STROKEWIDTH,
nbanks = 3, labelsize = 16, titlesize = 16,
tellheight = true, tellwidth = false, patchsize = (40.0f0, 20.0f0))
end
linkaxes!(axs...)
xmin = max(xmin, 10^-100)
xticks = __filter_nearzero(Makie.get_ticks(LogTicks(WilkinsonTicks(10; k_min = 5)),
__log10_zero, tickformatter, xmin, xmax))
yticks = __filter_nearzero(Makie.get_ticks(LogTicks(WilkinsonTicks(10; k_min = 5)),
__log10_zero, tickformatter, ymin, ymax))
foreach(axs) do ax
ax.xticks = xticks
ax.yticks = yticks
end
fig[0, :] = Label(fig, "Work-Precision Diagram for $(prob_name)",
fontsize = 24, tellwidth = false, font = :bold)
fig
end
return fig
endplot_collective_benchmark (generic function with 1 method)Benchmarks
We here run benchmarks for each of the 23 models.
Problem 1 (Generalized Rosenbrock function)
benchmark_problem!("Generalized Rosenbrock function")Error: UndefVarError: `solver_tracker` not definedProblem 2 (Powell singular function)
benchmark_problem!("Powell singular function")Error: UndefVarError: `solver_tracker` not definedProblem 3 (Powell badly scaled function)
benchmark_problem!("Powell badly scaled function")Error: UndefVarError: `solver_tracker` not definedProblem 4 (Wood function)
benchmark_problem!("Wood function")Error: UndefVarError: `solver_tracker` not definedProblem 5 (Helical valley function)
benchmark_problem!("Helical valley function")Error: UndefVarError: `solver_tracker` not definedProblem 6 (Watson function)
benchmark_problem!("Watson function")Error: UndefVarError: `solver_tracker` not definedProblem 7 (Chebyquad function)
benchmark_problem!("Chebyquad function")Error: UndefVarError: `solver_tracker` not definedProblem 8 (Brown almost linear function)
benchmark_problem!("Brown almost linear function")Error: UndefVarError: `solver_tracker` not definedProblem 9 (Discrete boundary value function)
benchmark_problem!("Discrete boundary value function")Error: UndefVarError: `solver_tracker` not definedProblem 10 (Discrete integral equation function)
benchmark_problem!("Discrete integral equation function")Error: UndefVarError: `solver_tracker` not definedProblem 11 (Trigonometric function)
benchmark_problem!("Trigonometric function")Error: UndefVarError: `solver_tracker` not definedProblem 12 (Variably dimensioned function)
benchmark_problem!("Variably dimensioned function")Error: UndefVarError: `solver_tracker` not definedProblem 13 (Broyden tridiagonal function)
benchmark_problem!("Broyden tridiagonal function")Error: UndefVarError: `solver_tracker` not definedProblem 14 (Broyden banded function)
benchmark_problem!("Broyden banded function")Error: UndefVarError: `solver_tracker` not definedProblem 15 (Hammarling 2 by 2 matrix square root problem)
benchmark_problem!("Hammarling 2 by 2 matrix square root problem")Error: UndefVarError: `solver_tracker` not definedProblem 16 (Hammarling 3 by 3 matrix square root problem)
benchmark_problem!("Hammarling 3 by 3 matrix square root problem")Error: UndefVarError: `solver_tracker` not definedProblem 17 (Dennis and Schnabel 2 by 2 example)
benchmark_problem!("Dennis and Schnabel 2 by 2 example")Error: UndefVarError: `solver_tracker` not definedProblem 18 (Sample problem 18)
benchmark_problem!("Sample problem 18")Error: UndefVarError: `solver_tracker` not definedProblem 19 (Sample problem 19)
benchmark_problem!("Sample problem 19")Error: UndefVarError: `solver_tracker` not definedProblem 20 (Scalar problem f(x) = x(x - 5)^2)
benchmark_problem!("Scalar problem f(x) = x(x - 5)^2")Error: UndefVarError: `solver_tracker` not definedProblem 21 (Freudenstein-Roth function)
benchmark_problem!("Freudenstein-Roth function")Error: UndefVarError: `solver_tracker` not definedProblem 22 (Boggs function)
benchmark_problem!("Boggs function")Error: UndefVarError: `solver_tracker` not definedProblem 23 (Chandrasekhar function)
benchmark_problem!("Chandrasekhar function")Error: UndefVarError: `solver_tracker` not definedSummary of successful solvers
Finally, we print a summary of which solvers successfully solved which problems.
solver_successes = [(solver.name in getfield.(prob[2], :name)) ? "O" : "X" for prob in solver_tracker, solver in solvers_all]
total_successes = [sum(solver_successes[:,i] .== "O") for i in 1:length(solvers_all)]
solver_outcomes = vcat(total_successes', solver_successes);Error: UndefVarError: `solver_tracker` not definedusing PrettyTables
io = IOBuffer()
println(io, "```@raw html")
pretty_table(io, solver_outcomes; backend = Val(:html), header = getfield.(solvers_all, :name), alignment=:c)
println(io, "```")
Base.Text(String(take!(io)))Error: UndefVarError: solvers_all not defined
Summary of General Solver Performance on All Problems
fig = begin
LINESTYLES = Dict(:nonlinearsolve => :solid, :simplenonlinearsolve => :dash,
:wrapper => :dot)
ASPECT_RATIO = 1
WIDTH = 1800
HEIGHT = round(Int, WIDTH * ASPECT_RATIO)
STROKEWIDTH = 2.5
colors = cgrad(:seaborn_bright, length(solvers_all); categorical = true)
cycle = Cycle([:marker], covary = true)
plot_theme = Theme(Lines = (; cycle), Scatter = (; cycle))
with_theme(plot_theme) do
fig = Figure(; size = (WIDTH, HEIGHT))
axs = Matrix{Any}(undef, 5, 5)
ls = []
scs = []
labels = []
solver_times = []
for i in 1:5, j in 1:5
idx = 5 * (i - 1) + j
idx > length(wp_general_tracker) && break
prob_name, wp = wp_general_tracker[idx]
ax = Axis(fig[i, j],
xscale = __log10_zero, yscale = __log10_zero,
xtickwidth = STROKEWIDTH,
ytickwidth = STROKEWIDTH, spinewidth = STROKEWIDTH,
title = prob_name, titlegap = 10,
xticklabelsize = 16, yticklabelsize = 16)
xmin, xmax, ymin, ymax = Inf, -Inf, Inf, -Inf
for wpᵢ in wp.wps
idx = findfirst(s -> s.name == wpᵢ.name, solvers_all)
errs = getindex.(wpᵢ.errors, :l∞)
times = wpᵢ.times
emin, emax = extrema(errs)
tmin, tmax = extrema(times)
emin < xmin && (xmin = emin)
emax > xmax && (xmax = emax)
tmin < ymin && (ymin = tmin)
tmax > ymax && (ymax = tmax)
l = lines!(ax, errs, times; color = colors[idx], linewidth = 5,
linestyle = LINESTYLES[solvers_all[idx].pkg], alpha = 0.8,
label = wpᵢ.name)
sc = scatter!(ax, errs, times; color = colors[idx], markersize = 16,
strokewidth = 2, marker = Cycled(idx), alpha = 0.8, label = wpᵢ.name)
if wpᵢ.name ∉ labels
push!(ls, l)
push!(scs, sc)
push!(labels, wpᵢ.name)
end
if wpᵢ.name ∈ first.(solver_times)
idxi = findfirst(x -> first(x) == wpᵢ.name, solver_times)
push!(solver_times[idxi][2], median(times) / length(wp.prob.u0))
else
push!(solver_times, wpᵢ.name => [median(times) / length(wp.prob.u0)])
end
end
xmin = max(xmin, 10^-100)
xticks = __filter_nearzero(Makie.get_ticks(LogTicks(WilkinsonTicks(5; k_min = 3)),
__log10_zero, tickformatter, xmin, xmax))
yticks = __filter_nearzero(Makie.get_ticks(LogTicks(WilkinsonTicks(5; k_min = 3)),
__log10_zero, tickformatter, ymin, ymax))
ax.xticks = xticks
ax.yticks = yticks
end
ordering = sortperm(median.(last.(solver_times)))
fig[0, :] = Label(fig, "Work-Precision Diagram for 23 Test Problems",
fontsize = 24, tellwidth = false, font = :bold)
fig[:, 0] = Label(fig, "Time (s)", fontsize = 20, tellheight = false, font = :bold,
rotation = π / 2)
fig[end + 1, :] = Label(fig,
L"Error: $\mathbf{||f(u^\ast)||_\infty}$",
fontsize = 20, tellwidth = false, font = :bold)
Legend(fig[5, 4:5], [[l, sc] for (l, sc) in zip(ls[ordering], scs[ordering])],
labels[ordering], "Successful Solvers";
framevisible=true, framewidth = STROKEWIDTH, orientation = :horizontal,
titlesize = 20, nbanks = 9, labelsize = 20, halign = :center,
tellheight = false, tellwidth = false, patchsize = (40.0f0, 20.0f0))
return fig
end
endError: UndefVarError: `solvers_all` not definedsave("summary_wp_23test_problems.svg", fig)Error: UndefVarError: `fig` not definedAppendix
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/NonlinearProblem","nonlinear_solver_23_tests.jmd")Computer Information:
Julia Version 1.10.6
Commit 67dffc4a8ae (2024-10-28 12:23 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
LIBM: libopenlibm
LLVM: libLLVM-15.0.7 (ORCJIT, znver2)
Threads: 1 default, 0 interactive, 1 GC (on 128 virtual cores)
Environment:
JULIA_CPU_THREADS = 128
JULIA_DEPOT_PATH = /cache/julia-buildkite-plugin/depots/5b300254-1738-4989-ae0a-f4d2d937f953
Package Information:
Status `/cache/build/exclusive-amdci1-0/julialang/scimlbenchmarks-dot-jl/benchmarks/NonlinearProblem/Project.toml`
[2169fc97] AlgebraicMultigrid v0.6.0
[6e4b80f9] BenchmarkTools v1.5.0
[13f3f980] CairoMakie v0.12.16
⌃ [2b5f629d] DiffEqBase v6.158.3
[f3b72e0c] DiffEqDevTools v2.45.1
⌃ [a0c0ee7d] DifferentiationInterface v0.6.22
⌃ [7da242da] Enzyme v0.13.14
[40713840] IncompleteLU v0.2.1
[b964fa9f] LaTeXStrings v1.4.0
[d3d80556] LineSearches v7.3.0
⌃ [7ed4a6bd] LinearSolve v2.36.2
[4854310b] MINPACK v1.3.0
[2774e3e8] NLsolve v4.5.1
[b7050fa9] NonlinearProblemLibrary v0.1.2
⌃ [8913a72c] NonlinearSolve v4.1.0
[ace2c81b] PETSc v0.3.1
[98d1487c] PolyesterForwardDiff v0.1.2
[08abe8d2] PrettyTables v2.4.0
[31c91b34] SciMLBenchmarks v0.1.3
[efcf1570] Setfield v1.1.1
[727e6d20] SimpleNonlinearSolve v2.0.0
[9f842d2f] SparseConnectivityTracer v0.6.8
[47a9eef4] SparseDiffTools v2.23.0
[f1835b91] SpeedMapping v0.3.0
[860ef19b] StableRNGs v1.0.2
[90137ffa] StaticArrays v1.9.8
[c3572dad] Sundials v4.26.1
⌃ [0c5d862f] Symbolics v6.18.3
Info Packages marked with ⌃ have new versions available and may be upgradable.
Warning The project dependencies or compat requirements have changed since the manifest was last resolved. It is recommended to `Pkg.resolve()` or consider `Pkg.update()` if necessary.And the full manifest:
Status `/cache/build/exclusive-amdci1-0/julialang/scimlbenchmarks-dot-jl/benchmarks/NonlinearProblem/Manifest.toml`
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[05823500] OpenLibm_jll v0.8.1+2
[efcefdf7] PCRE2_jll v10.42.0+1
[bea87d4a] SuiteSparse_jll v7.2.1+1
[83775a58] Zlib_jll v1.2.13+1
[8e850b90] libblastrampoline_jll v5.11.0+0
[8e850ede] nghttp2_jll v1.52.0+1
[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`
Warning The project dependencies or compat requirements have changed since the manifest was last resolved. It is recommended to `Pkg.resolve()` or consider `Pkg.update()` if necessary.