Refactor tests for CUTEstModel with benchmarks

Author: arnavk23

test/test_comprehensive.jl

@@ -3,12 +3,10 @@ using BenchmarkTools
 using LinearAlgebra
 using NLPModels
 
-# Test with an unconstrained problem first
 println("Testing with unconstrained problem (ROSENBR):")
 nlp1 = CUTEstModel{Float64}("ROSENBR")
 x0 = nlp1.meta.x0
 
-# Test hess_coord allocations
 println("✓ Problem loaded: $(nlp1.meta.name) ($(nlp1.meta.nvar) vars, $(nlp1.meta.ncon) cons)")
 vals_h = Vector{Float64}(undef, nlp1.meta.nnzh)
 hess_coord!(nlp1, x0, vals_h)
@@ -18,36 +16,30 @@ println("✓ hess_coord!: $(b_hess.allocs) allocations, $(BenchmarkTools.prettyt
 
 finalize(nlp1)
 
-# Test with a constrained problem
 println("\nTesting with constrained problem (HS6):")
 nlp2 = CUTEstModel{Float64}("HS6")
 x0 = nlp2.meta.x0
 
 println("✓ Problem loaded: $(nlp2.meta.name) ($(nlp2.meta.nvar) vars, $(nlp2.meta.ncon) cons)")
 println("  nnzj: $(nlp2.meta.nnzj), nnzh: $(nlp2.meta.nnzh)")
 
-# Test basic functionality
 f = obj(nlp2, x0)
 g = similar(x0)
 grad!(nlp2, x0, g)
 println("✓ obj/grad: f = $(round(f, digits=6)), ||g|| = $(round(norm(g), digits=6))")
 
-# Test constraint functions
 c = Vector{Float64}(undef, nlp2.meta.ncon)
 cons!(nlp2, x0, c)
 println("✓ constraints: ||c|| = $(round(norm(c), digits=6))")
 
-# Test cons_coord - this should show major allocation improvements
 println("\nTesting cons_coord allocation improvements:")
 c1, rows1, cols1, vals1 = cons_coord(nlp2, x0)
 
-# Benchmark cons_coord
 b_cons = @benchmark cons_coord($nlp2, $x0) samples=100 evals=5
 println("✓ cons_coord: $(b_cons.allocs) allocations, $(BenchmarkTools.prettytime(median(b_cons).time))")
 println("  Memory: $(BenchmarkTools.prettymemory(median(b_cons).memory))")
 println("  Returned $(length(vals1)) Jacobian elements")
 
-# Test cons_coord! 
 rows = Vector{Cint}(undef, nlp2.meta.nnzj)
 cols = Vector{Cint}(undef, nlp2.meta.nnzj)
 vals = Vector{Float64}(undef, nlp2.meta.nnzj)
@@ -56,7 +48,6 @@ c_out = Vector{Float64}(undef, nlp2.meta.ncon)
 b_cons_inplace = @benchmark cons_coord!($nlp2, $x0, $c_out, $rows, $cols, $vals) samples=100 evals=5
 println("✓ cons_coord!: $(b_cons_inplace.allocs) allocations, $(BenchmarkTools.prettytime(median(b_cons_inplace).time))")
 
-# Test type conversion
 println("\nTesting type conversion improvements:")
 x0_f32 = Float32.(x0)
 g_f32 = Vector{Float32}(undef, nlp2.meta.nvar)
@@ -65,7 +56,6 @@ b_grad_conv = @benchmark grad!($nlp2, $x0_f32, $g_f32) samples=100 evals=5
 println("✓ grad! with Float32->Float64 conversion: $(b_grad_conv.allocs) allocations")
 println("  Time: $(BenchmarkTools.prettytime(median(b_grad_conv).time))")
 
-# Test hess_coord with constraints
 vals_h2 = Vector{Float64}(undef, nlp2.meta.nnzh)
 y = zeros(nlp2.meta.ncon)
 hess_coord!(nlp2, x0, y, vals_h2)