CUTEst Unconstrained Nonlinear Optimization Benchmarks
Introduction
CUTEst, the Constrained and Unconstrained Testing Environment, is a collection of around 1500 problems for general nonlinear optimization used to test optimization routines. The wrapper CUTEst.jl provides convenient access to the problem collection, which we can leverage to test the optimizers made available by Optimization.jl.
Unconstrained problems
CUTEst contains 286 unconstrained problems. We will compare how the optimizers behave in terms of the time to solution with respect to the number of variables.
using Optimization
using OptimizationNLPModels
using CUTEst
using OptimizationOptimJL
using OptimizationOptimisers
using OptimizationOptimJL: LBFGS, ConjugateGradient, NelderMead, SimulatedAnnealing, ParticleSwarm
using Ipopt
using OptimizationMOI
using OptimizationMOI: MOI as MOI
using DataFrames
using Plots
using StatsPlots
using StatsBase: countmap
optimizers = [
("LBFGS", LBFGS()),
("ConjugateGradient", ConjugateGradient()),
("NelderMead", NelderMead()),
("SimulatedAnnealing", SimulatedAnnealing()),
("ParticleSwarm", ParticleSwarm()),
]
function get_stats(sol, optimizer_name)
if hasfield(typeof(sol), :stats) && hasfield(typeof(sol.stats), :time)
solve_time = sol.stats.time
else
solve_time = NaN
end
return (length(sol.u), solve_time, optimizer_name, Symbol(sol.retcode))
end
function run_benchmarks(problems, optimizers; chunk_size=1)
problem = String[]
n_vars = Int64[]
secs = Float64[]
solver = String[]
retcode = Symbol[]
optz = length(optimizers)
n = length(problems)
@info "Processing $(n) problems with $(optz) optimizers in chunks of $(chunk_size)"
broadcast(c -> sizehint!(c, optz * n), [problem, n_vars, secs, solver, retcode])
for chunk_start in 1:chunk_size:n
chunk_end = min(chunk_start + chunk_size - 1, n)
chunk_problems = problems[chunk_start:chunk_end]
@info "Processing chunk $(div(chunk_start-1, chunk_size)+1)/$(div(n-1, chunk_size)+1): problems $(chunk_start)-$(chunk_end)"
for (idx, prob_name) in enumerate(chunk_problems)
current_problem = chunk_start + idx - 1
@info "Problem $(current_problem)/$(n): $(prob_name)"
nlp_prob = nothing
try
nlp_prob = CUTEstModel(prob_name)
if nlp_prob.meta.nvar > 10000
@info " Skipping $(prob_name) (too large: $(nlp_prob.meta.nvar) variables)"
finalize(nlp_prob)
continue
end
prob = OptimizationNLPModels.OptimizationProblem(nlp_prob, Optimization.AutoFiniteDiff())
for (optimizer_name, optimizer) in optimizers
try
sol = solve(prob, optimizer; maxiters = 1000, maxtime = 30.0)
@info "✓ Solved $(prob_name) with $(optimizer_name) - Status: $(sol.retcode)"
vars, time, alg, code = get_stats(sol, optimizer_name)
push!(problem, prob_name)
push!(n_vars, vars)
push!(secs, time)
push!(solver, alg)
push!(retcode, code)
catch e
push!(problem, prob_name)
push!(n_vars, nlp_prob !== nothing ? nlp_prob.meta.nvar : -1)
push!(secs, NaN)
push!(solver, optimizer_name)
push!(retcode, :FAILED)
end
end
catch e
for (optimizer_name, optimizer) in optimizers
push!(problem, prob_name)
push!(n_vars, -1)
push!(secs, NaN)
push!(solver, optimizer_name)
push!(retcode, :LOAD_FAILED)
end
finally
if nlp_prob !== nothing
try
finalize(nlp_prob)
catch e
end
end
end
end
GC.gc()
@info "Completed chunk, memory usage cleaned up"
end
return DataFrame(problem = problem, n_vars = n_vars, secs = secs, solver = solver, retcode = retcode)
end
unc_problems = collect(CUTEst.select_sif_problems(contype="unc"))
println("Number of problems: ", length(unc_problems))
println("First 5 problems: ", unc_problems[1:min(5, end)])
unc_problems = unc_problems[1:min(50, length(unc_problems))]
println("Limited to ", length(unc_problems), " problems for comprehensive testing")
unc_results = run_benchmarks(unc_problems, optimizers)
@show unc_results
successful_codes = [:Success, :MaxIters, :MaxTime, :FirstOrderOptimal]
successful_results = filter(row -> row.retcode in successful_codes, unc_results)
total_attempts = nrow(unc_results)
successful_attempts = nrow(successful_results)
success_rate = total_attempts > 0 ? round(successful_attempts / total_attempts * 100, digits=1) : 0
println("SUCCESS RATE ANALYSIS:")
println("Total attempts: ", total_attempts)
println("Successful attempts: ", successful_attempts)
println("Success rate: ", success_rate, "%")
println("Return code distribution:")
if total_attempts > 0
for (code, count) in sort(collect(pairs(countmap(unc_results.retcode))), by=x->x[2], rev=true)
println(" ", code, ": ", count, " occurrences")
end
else
println(" No results to analyze")
end
@df unc_results scatter(:n_vars, :secs,
group = :solver,
xlabel = "n. variables",
ylabel = "secs.",
title = "Time to solution by optimizer and number of vars",
)Number of problems: 293
First 5 problems: ["LIARWHD", "SCHMVETT", "LUKSAN13LS", "VAREIGVL", "JUDGE"
]
Limited to 50 problems for comprehensive testing
unc_results = 240×5 DataFrame
Row │ problem n_vars secs solver retcode
│ String Int64 Float64 String Symbol
─────┼────────────────────────────────────────────────────────────────
1 │ LIARWHD 5000 1.28568 LBFGS Success
2 │ LIARWHD 5000 0.67915 ConjugateGradient Success
3 │ LIARWHD 5000 13.9192 NelderMead Failure
4 │ LIARWHD 5000 0.343245 SimulatedAnnealing Failure
5 │ LIARWHD 5000 33.5615 ParticleSwarm Failure
6 │ SCHMVETT 5000 0.13994 LBFGS Success
7 │ SCHMVETT 5000 0.095603 ConjugateGradient Success
8 │ SCHMVETT 5000 14.124 NelderMead Failure
9 │ SCHMVETT 5000 0.422547 SimulatedAnnealing Failure
10 │ SCHMVETT 5000 37.0636 ParticleSwarm Failure
11 │ LUKSAN13LS 98 0.0036242 LBFGS Success
12 │ LUKSAN13LS 98 0.00366998 ConjugateGradient Success
13 │ LUKSAN13LS 98 0.0117111 NelderMead Failure
14 │ LUKSAN13LS 98 0.00560713 SimulatedAnnealing Failure
15 │ LUKSAN13LS 98 0.442637 ParticleSwarm Failure
16 │ VAREIGVL 5000 0.0725961 LBFGS Success
17 │ VAREIGVL 5000 0.707599 ConjugateGradient Success
18 │ VAREIGVL 5000 13.5195 NelderMead Failure
19 │ VAREIGVL 5000 0.165379 SimulatedAnnealing Failure
20 │ VAREIGVL 5000 34.2195 ParticleSwarm Failure
21 │ JUDGE 2 0.000153065 LBFGS Success
22 │ JUDGE 2 0.000109911 ConjugateGradient Success
23 │ JUDGE 2 0.000216961 NelderMead Success
24 │ JUDGE 2 0.00303984 SimulatedAnnealing Failure
25 │ JUDGE 2 0.00658894 ParticleSwarm Failure
26 │ DIXMAANJ 3000 0.652868 LBFGS Failure
27 │ DIXMAANJ 3000 0.277309 ConjugateGradient Failure
28 │ DIXMAANJ 3000 5.12163 NelderMead Failure
29 │ DIXMAANJ 3000 0.0579059 SimulatedAnnealing Failure
30 │ DIXMAANJ 3000 31.0944 ParticleSwarm Failure
31 │ FBRAIN3LS 6 3.57757 LBFGS Failure
32 │ FBRAIN3LS 6 2.07655 ConjugateGradient Failure
33 │ FBRAIN3LS 6 0.559859 NelderMead Failure
34 │ FBRAIN3LS 6 0.362412 SimulatedAnnealing Failure
35 │ FBRAIN3LS 6 2.52477 ParticleSwarm Failure
36 │ SPIN2LS 102 0.039355 LBFGS Success
37 │ SPIN2LS 102 0.016027 ConjugateGradient Success
38 │ SPIN2LS 102 0.235501 NelderMead Failure
39 │ SPIN2LS 102 0.0204189 SimulatedAnnealing Failure
40 │ SPIN2LS 102 1.95174 ParticleSwarm Failure
41 │ SBRYBND 5000 2.17019 LBFGS Failure
42 │ SBRYBND 5000 0.958153 ConjugateGradient Failure
43 │ SBRYBND 5000 13.7104 NelderMead Failure
44 │ SBRYBND 5000 0.156268 SimulatedAnnealing Failure
45 │ SBRYBND 5000 33.7482 ParticleSwarm Failure
46 │ ARGLINC 200 0.05039 LBFGS Failure
47 │ ARGLINC 200 5.68835 ConjugateGradient Failure
48 │ ARGLINC 200 0.209398 NelderMead Failure
49 │ ARGLINC 200 0.0962369 SimulatedAnnealing Failure
50 │ ARGLINC 200 19.7177 ParticleSwarm Failure
51 │ TOINTGOR 50 0.00340319 LBFGS Success
52 │ TOINTGOR 50 0.00258589 ConjugateGradient Success
53 │ TOINTGOR 50 0.00784397 NelderMead Failure
54 │ TOINTGOR 50 0.00489497 SimulatedAnnealing Failure
55 │ TOINTGOR 50 0.147895 ParticleSwarm Failure
56 │ DIXMAANC 3000 0.00581217 LBFGS Success
57 │ DIXMAANC 3000 0.00369191 ConjugateGradient Success
58 │ DIXMAANC 3000 5.29081 NelderMead Failure
59 │ DIXMAANC 3000 0.058157 SimulatedAnnealing Failure
60 │ DIXMAANC 3000 30.9962 ParticleSwarm Failure
61 │ WAYSEA2 2 0.00019908 LBFGS Success
62 │ WAYSEA2 2 0.000185966 ConjugateGradient Success
63 │ WAYSEA2 2 0.000154018 NelderMead Success
64 │ WAYSEA2 2 0.00249887 SimulatedAnnealing Failure
65 │ WAYSEA2 2 0.00578904 ParticleSwarm Failure
66 │ BROWNDEN 4 0.000207901 LBFGS Success
67 │ BROWNDEN 4 0.000404119 ConjugateGradient Success
68 │ BROWNDEN 4 0.00077796 NelderMead Success
69 │ BROWNDEN 4 0.00321293 SimulatedAnnealing Failure
70 │ BROWNDEN 4 0.0077672 ParticleSwarm Failure
71 │ HILBERTA 2 5.91278e-5 LBFGS Success
72 │ HILBERTA 2 3.40939e-5 ConjugateGradient Success
73 │ HILBERTA 2 0.000133991 NelderMead Success
74 │ HILBERTA 2 0.00270796 SimulatedAnnealing Failure
75 │ HILBERTA 2 0.00593019 ParticleSwarm Failure
76 │ DMN37142LS 66 11.9498 LBFGS Failure
77 │ DMN37142LS 66 8.76718 ConjugateGradient Failure
78 │ DMN37142LS 66 0.977412 NelderMead Failure
79 │ DMN37142LS 66 0.617031 SimulatedAnnealing Failure
80 │ DMN37142LS 66 30.0747 ParticleSwarm Failure
81 │ DIXMAANE1 3000 0.128823 LBFGS Success
82 │ DIXMAANE1 3000 0.0775619 ConjugateGradient Success
83 │ DIXMAANE1 3000 5.15211 NelderMead Failure
84 │ DIXMAANE1 3000 0.0447609 SimulatedAnnealing Failure
85 │ DIXMAANE1 3000 30.8996 ParticleSwarm Failure
86 │ PALMER5D 4 0.000102043 LBFGS Success
87 │ PALMER5D 4 8.82149e-5 ConjugateGradient Success
88 │ PALMER5D 4 0.00101995 NelderMead Success
89 │ PALMER5D 4 0.00291109 SimulatedAnnealing Failure
90 │ PALMER5D 4 0.00706482 ParticleSwarm Failure
91 │ BOXBODLS 2 0.000302792 LBFGS Success
92 │ BOXBODLS 2 0.000472069 ConjugateGradient Success
93 │ BOXBODLS 2 0.00027895 NelderMead Success
94 │ BOXBODLS 2 0.00458908 SimulatedAnnealing Failure
95 │ BOXBODLS 2 0.01073 ParticleSwarm Failure
96 │ HIMMELBB 2 0.000123978 LBFGS Success
97 │ HIMMELBB 2 0.000169039 ConjugateGradient Success
98 │ HIMMELBB 2 9.20296e-5 NelderMead Success
99 │ HIMMELBB 2 0.00303292 SimulatedAnnealing Failure
100 │ HIMMELBB 2 0.00590086 ParticleSwarm Failure
101 │ ENGVAL2 3 0.000246048 LBFGS Success
102 │ ENGVAL2 3 0.000607967 ConjugateGradient Success
103 │ ENGVAL2 3 0.000550032 NelderMead Success
104 │ ENGVAL2 3 0.00315118 SimulatedAnnealing Failure
105 │ ENGVAL2 3 0.00574422 ParticleSwarm Failure
106 │ MUONSINELS 1 0.0962291 LBFGS Failure
107 │ MUONSINELS 1 0.000657082 ConjugateGradient Success
108 │ MUONSINELS 1 0.000166178 NelderMead Success
109 │ MUONSINELS 1 0.015193 SimulatedAnnealing Failure
110 │ MUONSINELS 1 0.05567 ParticleSwarm Failure
111 │ ENSOLS 9 0.00483084 LBFGS Success
112 │ ENSOLS 9 0.00836897 ConjugateGradient Success
113 │ ENSOLS 9 0.0357702 NelderMead Success
114 │ ENSOLS 9 0.0264409 SimulatedAnnealing Failure
115 │ ENSOLS 9 0.239312 ParticleSwarm Failure
116 │ PRICE4 2 0.00012207 LBFGS Success
117 │ PRICE4 2 0.00013113 ConjugateGradient Success
118 │ PRICE4 2 0.000201941 NelderMead Success
119 │ PRICE4 2 0.00308895 SimulatedAnnealing Failure
120 │ PRICE4 2 0.00597906 ParticleSwarm Failure
121 │ EIGENALS 2550 12.3316 LBFGS Failure
122 │ EIGENALS 2550 4.76671 ConjugateGradient Failure
123 │ EIGENALS 2550 5.6711 NelderMead Failure
124 │ EIGENALS 2550 0.565485 SimulatedAnnealing Failure
125 │ EIGENALS 2550 31.6766 ParticleSwarm Failure
126 │ CERI651ELS 7 0.0300519 LBFGS Success
127 │ CERI651ELS 7 0.068429 ConjugateGradient Failure
128 │ CERI651ELS 7 6.31809e-5 NelderMead Failure
129 │ CERI651ELS 7 0.00650382 SimulatedAnnealing Failure
130 │ CERI651ELS 7 0.036571 ParticleSwarm Failure
131 │ GENHUMPS 5000 2.76571 LBFGS Failure
132 │ GENHUMPS 5000 2.07636 ConjugateGradient Failure
133 │ GENHUMPS 5000 15.3474 NelderMead Failure
134 │ GENHUMPS 5000 0.444946 SimulatedAnnealing Failure
135 │ GENHUMPS 5000 32.9061 ParticleSwarm Failure
136 │ OSCIPATH 500 0.00153208 LBFGS Success
137 │ OSCIPATH 500 0.00119996 ConjugateGradient Success
138 │ OSCIPATH 500 0.102505 NelderMead Failure
139 │ OSCIPATH 500 0.0119679 SimulatedAnnealing Failure
140 │ OSCIPATH 500 9.81063 ParticleSwarm Failure
141 │ FLETCBV2 5000 1.93324 LBFGS Failure
142 │ FLETCBV2 5000 0.843986 ConjugateGradient Failure
143 │ FLETCBV2 5000 14.1034 NelderMead Failure
144 │ FLETCBV2 5000 0.261064 SimulatedAnnealing Failure
145 │ FLETCBV2 5000 35.4844 ParticleSwarm Failure
146 │ DIXMAAND 3000 0.00723696 LBFGS Success
147 │ DIXMAAND 3000 0.00511599 ConjugateGradient Success
148 │ DIXMAAND 3000 5.36648 NelderMead Failure
149 │ DIXMAAND 3000 0.0588238 SimulatedAnnealing Failure
150 │ DIXMAAND 3000 30.771 ParticleSwarm Failure
151 │ SISSER 2 0.00010705 LBFGS Success
152 │ SISSER 2 0.000102997 ConjugateGradient Success
153 │ SISSER 2 9.48906e-5 NelderMead Success
154 │ SISSER 2 0.00296092 SimulatedAnnealing Failure
155 │ SISSER 2 0.00613713 ParticleSwarm Failure
156 │ TRIGON1 10 0.000524044 LBFGS Success
157 │ TRIGON1 10 0.00088501 ConjugateGradient Success
158 │ TRIGON1 10 0.00461102 NelderMead Success
159 │ TRIGON1 10 0.00534678 SimulatedAnnealing Failure
160 │ TRIGON1 10 0.02597 ParticleSwarm Failure
161 │ S308NE 2 NaN LBFGS FAILED
162 │ S308NE 2 NaN ConjugateGradient FAILED
163 │ S308NE 2 NaN NelderMead FAILED
164 │ S308NE 2 NaN SimulatedAnnealing FAILED
165 │ S308NE 2 NaN ParticleSwarm FAILED
166 │ PENALTY1 1000 0.0107739 LBFGS Success
167 │ PENALTY1 1000 0.0050981 ConjugateGradient Success
168 │ PENALTY1 1000 0.246242 NelderMead Failure
169 │ PENALTY1 1000 0.0176408 SimulatedAnnealing Failure
170 │ PENALTY1 1000 30.0495 ParticleSwarm Failure
171 │ LRW8A 300 21.9441 LBFGS Failure
172 │ LRW8A 300 10.074 ConjugateGradient Failure
173 │ LRW8A 300 3.87486 NelderMead Failure
174 │ LRW8A 300 2.68621 SimulatedAnnealing Failure
175 │ LRW8A 300 30.8375 ParticleSwarm Failure
176 │ SPMSRTLS 4999 0.460211 LBFGS Success
177 │ SPMSRTLS 4999 0.200958 ConjugateGradient Success
178 │ SPMSRTLS 4999 14.2561 NelderMead Failure
179 │ SPMSRTLS 4999 0.128738 SimulatedAnnealing Failure
180 │ SPMSRTLS 4999 33.1972 ParticleSwarm Failure
181 │ NONCVXUN 5000 2.37591 LBFGS Failure
182 │ NONCVXUN 5000 1.05323 ConjugateGradient Failure
183 │ NONCVXUN 5000 14.6716 NelderMead Failure
184 │ NONCVXUN 5000 0.304772 SimulatedAnnealing Failure
185 │ NONCVXUN 5000 34.0975 ParticleSwarm Failure
186 │ BRYBND 5000 0.105322 LBFGS Success
187 │ BRYBND 5000 0.037384 ConjugateGradient Success
188 │ BRYBND 5000 14.3121 NelderMead Failure
189 │ BRYBND 5000 0.141034 SimulatedAnnealing Failure
190 │ BRYBND 5000 33.1568 ParticleSwarm Failure
191 │ GROWTHLS 3 6.79493e-5 LBFGS Success
192 │ GROWTHLS 3 3.69549e-5 ConjugateGradient Success
193 │ GROWTHLS 3 0.00138998 NelderMead Success
194 │ GROWTHLS 3 0.003443 SimulatedAnnealing Failure
195 │ GROWTHLS 3 0.0117629 ParticleSwarm Failure
196 │ SINEVAL 2 0.000437021 LBFGS Success
197 │ SINEVAL 2 0.000370026 ConjugateGradient Success
198 │ SINEVAL 2 0.000664949 NelderMead Success
199 │ SINEVAL 2 0.00316405 SimulatedAnnealing Failure
200 │ SINEVAL 2 0.00663614 ParticleSwarm Failure
201 │ GAUSS2LS 8 0.00978899 LBFGS Success
202 │ GAUSS2LS 8 0.0712101 ConjugateGradient Failure
203 │ GAUSS2LS 8 0.0122428 NelderMead Success
204 │ GAUSS2LS 8 0.014828 SimulatedAnnealing Failure
205 │ GAUSS2LS 8 0.12431 ParticleSwarm Failure
206 │ STRATEC 10 3.23459 LBFGS Success
207 │ STRATEC 10 8.13048 ConjugateGradient Failure
208 │ STRATEC 10 0.460869 NelderMead Failure
209 │ STRATEC 10 0.324659 SimulatedAnnealing Failure
210 │ STRATEC 10 3.54688 ParticleSwarm Failure
211 │ NELSONLS 3 0.0416131 LBFGS Failure
212 │ NELSONLS 3 0.002249 ConjugateGradient Success
213 │ NELSONLS 3 0.00459981 NelderMead Success
214 │ NELSONLS 3 0.00610399 SimulatedAnnealing Failure
215 │ NELSONLS 3 0.015919 ParticleSwarm Failure
216 │ HYDCAR6LS 29 0.0344939 LBFGS Failure
217 │ HYDCAR6LS 29 0.0175531 ConjugateGradient Failure
218 │ HYDCAR6LS 29 0.00751591 NelderMead Failure
219 │ HYDCAR6LS 29 0.00569606 SimulatedAnnealing Failure
220 │ HYDCAR6LS 29 0.0998991 ParticleSwarm Failure
221 │ DQRTIC 5000 0.0313861 LBFGS Success
222 │ DQRTIC 5000 0.00918698 ConjugateGradient Success
223 │ DQRTIC 5000 13.8571 NelderMead Failure
224 │ DQRTIC 5000 0.06336 SimulatedAnnealing Failure
225 │ DQRTIC 5000 34.256 ParticleSwarm Failure
226 │ MISRA1ALS 2 0.000996113 LBFGS Success
227 │ MISRA1ALS 2 0.00155616 ConjugateGradient Success
228 │ MISRA1ALS 2 0.00100899 NelderMead Success
229 │ MISRA1ALS 2 0.00509691 SimulatedAnnealing Failure
230 │ MISRA1ALS 2 0.01249 ParticleSwarm Failure
231 │ WAYSEA1 2 0.000146151 LBFGS Success
232 │ WAYSEA1 2 0.000123978 ConjugateGradient Success
233 │ WAYSEA1 2 0.000144005 NelderMead Success
234 │ WAYSEA1 2 0.00302601 SimulatedAnnealing Failure
235 │ WAYSEA1 2 0.00728106 ParticleSwarm Failure
236 │ BOX 10000 0.0220361 LBFGS Success
237 │ BOX 10000 0.042733 ConjugateGradient Success
238 │ BOX 10000 33.1561 NelderMead Failure
239 │ BOX 10000 0.386975 SimulatedAnnealing Failure
240 │ BOX 10000 38.1831 ParticleSwarm Failure
SUCCESS RATE ANALYSIS:
Total attempts: 240
Successful attempts: 86
Success rate: 35.8%
Return code distribution:
Failure: 149 occurrences
Success: 86 occurrences
FAILED: 5 occurrences
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/OptimizationCUTEst","CUTEst_unconstrained.jmd")Computer Information:
Julia Version 1.10.10
Commit 95f30e51f41 (2025-06-27 09:51 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: 128 default, 0 interactive, 64 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-amdci3-0/julialang/scimlbenchmarks-dot-jl/benchmarks/OptimizationCUTEst/Project.toml`
⌃ [1b53aba6] CUTEst v1.3.2
⌃ [a93c6f00] DataFrames v1.7.0
⌃ [b6b21f68] Ipopt v1.10.6
⌃ [b8f27783] MathOptInterface v1.42.1
⌃ [a4795742] NLPModels v0.21.5
⌅ [7f7a1694] Optimization v4.5.0
⌃ [fd9f6733] OptimizationMOI v0.5.5
⌅ [064b21be] OptimizationNLPModels v0.0.2
⌃ [36348300] OptimizationOptimJL v0.4.3
⌃ [42dfb2eb] OptimizationOptimisers v0.3.8
⌃ [91a5bcdd] Plots v1.40.17
[31c91b34] SciMLBenchmarks v0.1.3
⌃ [2913bbd2] StatsBase v0.34.6
⌃ [f3b207a7] StatsPlots v0.15.7
[de0858da] Printf
[10745b16] Statistics v1.10.0
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`
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-amdci3-0/julialang/scimlbenchmarks-dot-jl/benchmarks/OptimizationCUTEst/Manifest.toml`
⌃ [47edcb42] ADTypes v1.16.0
[621f4979] AbstractFFTs v1.5.0
[1520ce14] AbstractTrees v0.4.5
⌃ [7d9f7c33] Accessors v0.1.42
⌃ [79e6a3ab] Adapt v4.3.0
[66dad0bd] AliasTables v1.1.3
[ec485272] ArnoldiMethod v0.4.0
[7d9fca2a] Arpack v0.5.4
⌃ [4fba245c] ArrayInterface v7.19.0
⌃ [4c555306] ArrayLayouts v1.11.2
[13072b0f] AxisAlgorithms v1.1.0
⌃ [6e4b80f9] BenchmarkTools v1.6.0
[e2ed5e7c] Bijections v0.2.2
[d1d4a3ce] BitFlags v0.1.9
[62783981] BitTwiddlingConvenienceFunctions v0.1.6
⌃ [8e7c35d0] BlockArrays v1.7.0
⌃ [70df07ce] BracketingNonlinearSolve v1.3.0
⌃ [2a0fbf3d] CPUSummary v0.2.6
⌃ [1b53aba6] CUTEst v1.3.2
⌃ [d360d2e6] ChainRulesCore v1.25.2
[fb6a15b2] CloseOpenIntervals v0.1.13
[aaaa29a8] Clustering v0.15.8
[523fee87] CodecBzip2 v0.8.5
[944b1d66] CodecZlib v0.7.8
⌃ [35d6a980] ColorSchemes v3.30.0
[3da002f7] ColorTypes v0.12.1
[c3611d14] ColorVectorSpace v0.11.0
[5ae59095] Colors v0.13.1
⌃ [861a8166] Combinatorics v1.0.3
⌅ [a80b9123] CommonMark v0.9.1
⌃ [38540f10] CommonSolve v0.2.4
[bbf7d656] CommonSubexpressions v0.3.1
[f70d9fcc] CommonWorldInvalidations v1.0.0
⌃ [34da2185] Compat v4.18.0
[b152e2b5] CompositeTypes v0.1.4
[a33af91c] CompositionsBase v0.1.2
[2569d6c7] ConcreteStructs v0.2.3
[f0e56b4a] ConcurrentUtilities v2.5.0
⌃ [8f4d0f93] Conda v1.10.2
[88cd18e8] ConsoleProgressMonitor v0.1.2
[187b0558] ConstructionBase v1.6.0
[d38c429a] Contour v0.6.3
[adafc99b] CpuId v0.3.1
[a8cc5b0e] Crayons v4.1.1
[9a962f9c] DataAPI v1.16.0
⌃ [a93c6f00] DataFrames v1.7.0
⌅ [864edb3b] DataStructures v0.18.22
[e2d170a0] DataValueInterfaces v1.0.0
[8bb1440f] DelimitedFiles v1.9.1
⌃ [2b5f629d] DiffEqBase v6.181.0
⌃ [459566f4] DiffEqCallbacks v4.8.0
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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`
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