adding failing reset!

Author: arnavk23

tutorials/introduction-to-solverbenchmark/Project.toml

@@ -1,6 +1,7 @@
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 NLPModelsTest = "7998695d-6960-4d3a-85c4-e1bceb8cd856"
+NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
@@ -12,6 +13,7 @@ SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 
 DataFrames = "1.3.4"
 NLPModelsTest = "0.9"
+NLPModels = "0.20"
 Plots = "1.31.7"
 PyPlot = "2.10.0"
 SolverBenchmark = "0.5.3"

tutorials/introduction-to-solverbenchmark/index.jmd

@@ -228,18 +228,20 @@ If a solver's `GenericExecutionStats` contains a `solver_specific` dictionary, t
 
 Here is an example showing how to populate `solver_specific` and place those fields in a stats table:
 ```julia
-using ADNLPModels, DataFrames, SolverCore
-
-nlp = ADNLPModel(x -> sum(x .^ 2), [1.0, 1.0])
-stats = SolverCore.GenericExecutionStats(nlp)
-SolverCore.set_solver_specific!(stats, :isConvex, true)
-SolverCore.set_solver_specific!(stats, :inner_iterations, 7)
-
-df = DataFrame(
-  solver = ["newton"],
-  status = [stats.status],
-  isConvex = [stats.solver_specific[:isConvex]],
-  inner_iterations = [stats.solver_specific[:inner_iterations]],
-)
-pretty_stats(stdout, df)
+import NLPModels: reset!
+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)
+
+# Access the solver-specific column `:isConvex` for the `:newton` solver
+df_newton = stats[:newton]
+df_newton.isConvex
 ```