nlp = woods(n = 10)

global nbsolver = 2
for solver in ALL_solvers
    global nbsolver += 1
    println(nbsolver, "  ", solver)
    (x, f, gNorm, iter, optimal, tired, status) = solver(nlp, verbose = true)
    @printf(
        "%-15s  %8d  %9.2e  %7.1e  %5d  %5d  %6d  %s\n",
        nlp.meta.name,
        nlp.meta.nvar,
        f,
        gNorm,
        nlp.counters.neval_obj,
        nlp.counters.neval_grad,
        nlp.counters.neval_hprod,
        status
    )    #stats = run_solver(solver, model, verbose=false)
    #@test (all([stats...] .>= 0))
    @test optimal
    reset!(nlp)
end
