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solver benchmark: specifying solver-specific options #152

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solver benchmark: specifying solver-specific options
arnavk23 Oct 30, 2025
9ad3b2b
Redoing index.jmd and Project.toml to fix SolverBenchmark tutorial
arnavk23 Oct 30, 2025
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3 changes: 3 additions & 0 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,12 @@ version = "0.2.0"

[deps]
Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a"
InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
JSOSolvers = "10dff2fc-5484-5881-a0e0-c90441020f8a"
Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
NLPModelsTest = "7998695d-6960-4d3a-85c4-e1bceb8cd856"
Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
Weave = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9"
YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
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32 changes: 32 additions & 0 deletions tutorials/introduction-to-solverbenchmark/index.jmd
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Expand Up @@ -221,3 +221,35 @@ p = profile_solvers(stats, costs, costnames)
Here is a useful tutorial on how to use the benchmark with specific solver:
[Run a benchmark with OptimizationProblems](https://jso.dev/OptimizationProblems.jl/dev/benchmark/)
The tutorial covers how to use the problems from `OptimizationProblems` to run a benchmark for unconstrained optimization.

## Handling `solver_specific` in benchmark runs
If a solver's execution-stats object contains a `solver_specific` dictionary
(accessible as `s.solver_specific`), `solve_problems` will create columns for
each key in that dictionary in the per-solver `DataFrame`. (Note: `bmark_solvers`
forwards keyword arguments to each solver, so ensure your solver wrapper populates
`s.solver_specific` if you want those columns.)

Here is a example showing how to set a solver-specific flag and then access it for tabulation:
```julia
using NLPModelsTest, DataFrames, SolverCore, SolverBenchmark

function newton(nlp)
stats = GenericExecutionStats(nlp)
set_solver_specific!(stats, :isConvex, true)
return stats
end

solvers = Dict(:newton => newton)
problems = [NLPModelsTest.BROWNDEN()]
stats = bmark_solvers(solvers, problems)

combined_stats = DataFrame(
name = stats[:newton].name,
nvars = stats[:newton].nvar,
convex = stats[:newton].isConvex,
newton_iters = stats[:newton].iter,
)

combined_stats.convex = string.(combined_stats.convex)
pretty_stats(combined_stats)
```