solver benchmark: specifying solver-specific options

Project.toml

@@ -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"

tutorials/introduction-to-solverbenchmark/index.jmd

@@ -221,3 +221,33 @@ 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 stats
+If a solver's GenericExecutionStats contains a `solver_specific` dictionary
+, a column is created for
+each key in that dictionary in the per-solver `DataFrame`.
+
+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)
+```