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Oct 21, 2025
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Remove NNMF problem and update Benchmark.jl accordingly #254

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75dd44e
Remove NNMF problem and update Benchmark.jl accordingly
MohamedLaghdafHABIBOULLAH Oct 20, 2025
fe6762f
Refactor SVM benchmark output formatting and improve LaTeX table gene…
MohamedLaghdafHABIBOULLAH Oct 21, 2025
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199 changes: 20 additions & 179 deletions paper/examples/Benchmark.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,6 @@
# ======== IMPORTS ======== #
#############################
using Random, LinearAlgebra
using ProximalOperators, ProximalCore, ProximalAlgorithms
using ShiftedProximalOperators
using NLPModels, NLPModelsModifiers
using RegularizedOptimization, RegularizedProblems
Expand All @@ -14,7 +13,7 @@ using LaTeXStrings

# Local includes
include("comparison-config.jl")
using .ComparisonConfig: CFG, CFG2
using .ComparisonConfig: CFG

#############################
# ===== Helper utils ====== #
Expand Down Expand Up @@ -58,7 +57,7 @@ function run_tr_svm!(model, x0; λ = 1.0, qn = :LSR1, atol = 1e-3, rtol = 1e-3,
t = @elapsed RegularizedOptimization.solve!(solver, reg_nlp, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose, sub_kwargs = sub_kwargs)
return (
name = "TR ($(String(qn)), SVM)",
name = "TR",
status = string(stats.status),
time = t,
iters = get(stats.solver_specific, :outer_iter, missing),
Expand All @@ -84,7 +83,7 @@ function run_r2n_svm!(model, x0; λ = 1.0, qn = :LBFGS, atol = 1e-3, rtol = 1e-3
t = @elapsed RegularizedOptimization.solve!(solver, reg_nlp, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose, sub_kwargs = sub_kwargs)
return (
name = "R2N ($(String(qn)), SVM)",
name = "R2N",
status = string(stats.status),
time = t,
iters = get(stats.solver_specific, :outer_iter, missing),
Expand All @@ -108,7 +107,7 @@ function run_LM_svm!(nls_model, x0; λ = 1.0, atol = 1e-3, rtol = 1e-3, verbose
t = @elapsed RegularizedOptimization.solve!(solver, reg_nls, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose, sub_kwargs = sub_kwargs)
return (
name = "LM (SVM)",
name = "LM",
status = string(stats.status),
time = t,
iters = get(stats.solver_specific, :outer_iter, missing),
Expand All @@ -132,7 +131,7 @@ function run_LMTR_svm!(nls_model, x0; λ = 1.0, atol = 1e-3, rtol = 1e-3, verbos
t = @elapsed RegularizedOptimization.solve!(solver, reg_nls, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose, sub_kwargs = sub_kwargs)
return (
name = "LMTR (SVM)",
name = "LMTR",
status = string(stats.status),
time = t,
iters = get(stats.solver_specific, :outer_iter, missing),
Expand All @@ -159,18 +158,14 @@ function bench_svm!(cfg = CFG)
println("\n=== SVM: solver comparison ===")
for m in results
println("\n→ ", m.name)
println(" status = ", m.status)
println(" time (s) = ", round(m.time, digits = 4))
m.iters !== missing && println(" outer iters = ", m.iters)
println(" # f eval = ", m.fevals)
println(" # ∇f eval = ", m.gevals)
m.proxcalls !== missing && println(" # prox calls = ", Int(m.proxcalls))
println(" final objective= ", round(obj(model, m.solution), digits = 4))
println(" accuracy (%) = ", round(acc(residual(nls_train, m.solution)), digits = 1))
println(" status = ", m.status)
println(" time (s) = ", round(m.time, digits = 4))
println(" # f eval = ", m.fevals)
println(" # ∇f eval = ", m.gevals)
m.proxcalls !== missing && println(" # prox calls = ", Int(m.proxcalls))
println(" final objective = ", round(obj(model, m.solution), digits = 4))
end

println("\nSVM Config:"); print_config(cfg)

data_svm = [
(; name=m.name,
status=string(m.status),
Expand All @@ -185,182 +180,28 @@ function bench_svm!(cfg = CFG)
return data_svm
end

#############################
# ======= NNMF bench ====== #
#############################

function run_tr_nnmf!(model, x0; λ = 1.0, qn = :LSR1, atol = 1e-3, rtol = 1e-3, verbose = 0, sub_kwargs = (;), selected = nothing)
qn_model = ensure_qn(model, qn)
reset!(qn_model)
reg_nlp = RegularizedNLPModel(qn_model, NormL0(λ), selected)
solver = TRSolver(reg_nlp)
stats = RegularizedExecutionStats(reg_nlp)
RegularizedOptimization.solve!(solver, reg_nlp, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose, sub_kwargs = sub_kwargs)
reset!(qn_model) # Reset counters before timing
reg_nlp = RegularizedNLPModel(qn_model, NormL0(λ), selected) # Re-create to reset prox eval count
solver = TRSolver(reg_nlp)
t = @elapsed RegularizedOptimization.solve!(solver, reg_nlp, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose, sub_kwargs = sub_kwargs)
return (
name = "TR ($(String(qn)), NNMF)",
status = string(stats.status),
time = t,
iters = get(stats.solver_specific, :outer_iter, missing),
fevals = neval_obj(qn_model),
gevals = neval_grad(qn_model),
proxcalls = get(stats.solver_specific, :prox_evals, missing),
solution = stats.solution,
final_obj = obj(model, stats.solution)
)
end

function run_r2n_nnmf!(model, x0; λ = 1.0, qn = :LBFGS, atol = 1e-3, rtol = 1e-3, verbose = 0, sub_kwargs = (;), σk = 1e5, selected = nothing)
qn_model = ensure_qn(model, qn)
reset!(qn_model)
reg_nlp = RegularizedNLPModel(qn_model, NormL0(λ), selected)
solver = R2NSolver(reg_nlp)
stats = RegularizedExecutionStats(reg_nlp)
RegularizedOptimization.solve!(solver, reg_nlp, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose,
sub_kwargs = sub_kwargs)

reset!(qn_model) # Reset counters before timing
reg_nlp = RegularizedNLPModel(qn_model, NormL0(λ), selected) # Re-create to reset prox eval count
solver = R2NSolver(reg_nlp)
t = @elapsed RegularizedOptimization.solve!(solver, reg_nlp, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose,
sub_kwargs = sub_kwargs)
return (
name = "R2N ($(String(qn)), NNMF)",
status = string(stats.status),
time = t,
iters = get(stats.solver_specific, :outer_iter, missing),
fevals = neval_obj(qn_model),
gevals = neval_grad(qn_model),
proxcalls = get(stats.solver_specific, :prox_evals, missing),
solution = stats.solution,
final_obj = obj(model, stats.solution)
)
end

function run_LM_nnmf!(nls_model, x0; λ = 1.0, atol = 1e-3, rtol = 1e-3, verbose = 0, selected = nothing, sub_kwargs = (;))
reg_nls = RegularizedNLSModel(nls_model, NormL0(λ), selected)
solver = LMSolver(reg_nls)
stats = RegularizedExecutionStats(reg_nls)
RegularizedOptimization.solve!(solver, reg_nls, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose, sub_kwargs = sub_kwargs)
reset!(nls_model) # Reset counters before timing
reg_nls = RegularizedNLSModel(nls_model, NormL0(λ), selected)
solver = LMSolver(reg_nls)
t = @elapsed RegularizedOptimization.solve!(solver, reg_nls, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose, sub_kwargs = sub_kwargs)
return (
name = "LM (NNMF)",
status = string(stats.status),
time = t,
iters = get(stats.solver_specific, :outer_iter, missing),
fevals = neval_residual(nls_model),
gevals = neval_jtprod_residual(nls_model) + neval_jprod_residual(nls_model),
proxcalls = get(stats.solver_specific, :prox_evals, missing),
solution = stats.solution,
final_obj = obj(nls_model, stats.solution)
)
end

function run_LMTR_nnmf!(nls_model, x0; λ = 1.0, atol = 1e-3, rtol = 1e-3, verbose = 0, selected = nothing, sub_kwargs = (;))
reg_nls = RegularizedNLSModel(nls_model, NormL0(λ), selected)
solver = LMTRSolver(reg_nls)
stats = RegularizedExecutionStats(reg_nls)
RegularizedOptimization.solve!(solver, reg_nls, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose, sub_kwargs = sub_kwargs)
reset!(nls_model) # Reset counters before timing
reg_nls = RegularizedNLSModel(nls_model, NormL0(λ), selected)
solver = LMTRSolver(reg_nls)
t = @elapsed RegularizedOptimization.solve!(solver, reg_nls, stats;
x = x0, atol = atol, rtol = rtol, verbose = verbose, sub_kwargs = sub_kwargs)
return (
name = "LMTR (NNMF)",
status = string(stats.status),
time = t,
iters = get(stats.solver_specific, :outer_iter, missing),
fevals = neval_residual(nls_model),
gevals = neval_jtprod_residual(nls_model) + neval_jprod_residual(nls_model),
proxcalls = get(stats.solver_specific, :prox_evals, missing),
solution = stats.solution,
final_obj = obj(nls_model, stats.solution)
)
end

function bench_nnmf!(cfg = CFG2; m = 100, n = 50, k = 5)
Random.seed!(cfg.SEED)

model, nls_model, _, selected = nnmf_model(m, n, k)

# build x0 on positive orthant as original
x0 = max.(rand(model.meta.nvar), 0.0)

# heuristic lambda (copied logic)
cfg.LAMBDA_L0 = norm(grad(model, rand(model.meta.nvar)), Inf) / 200

results = NamedTuple[]
(:TR in cfg.RUN_SOLVERS) && push!(results, run_tr_nnmf!(model, x0; λ = cfg.LAMBDA_L0, qn = cfg.QN_FOR_TR, atol = cfg.TOL, rtol = cfg.RTOL, verbose = cfg.VERBOSE_RO, sub_kwargs = cfg.SUB_KWARGS_R2N, selected = selected))
(:R2N in cfg.RUN_SOLVERS) && push!(results, run_r2n_nnmf!(model, x0; λ = cfg.LAMBDA_L0, qn = cfg.QN_FOR_R2N, atol = cfg.TOL, rtol = cfg.RTOL, verbose = cfg.VERBOSE_RO, sub_kwargs = cfg.SUB_KWARGS_R2N, selected = selected))
(:LM in cfg.RUN_SOLVERS) && push!(results, run_LM_nnmf!(nls_model, x0; λ = cfg.LAMBDA_L0, atol = cfg.TOL, rtol = cfg.RTOL, verbose = cfg.VERBOSE_RO, selected = selected, sub_kwargs = cfg.SUB_KWARGS_R2N))
(:LMTR in cfg.RUN_SOLVERS) && push!(results, run_LMTR_nnmf!(nls_model, x0; λ = cfg.LAMBDA_L0, atol = cfg.TOL, rtol = cfg.RTOL, verbose = cfg.VERBOSE_RO, selected = selected, sub_kwargs = cfg.SUB_KWARGS_R2N))

println("\n=== NNMF: solver comparison ===")
for m in results
println("\n→ ", m.name)
println(" status = ", m.status)
println(" time (s) = ", round(m.time, digits = 4))
m.iters !== missing && println(" outer iters = ", m.iters)
println(" # f eval = ", m.fevals)
println(" # ∇f eval = ", m.gevals)
m.proxcalls !== missing && println(" # prox calls = ", Int(m.proxcalls))
println(" final objective= ", round(obj(model, m.solution), digits = 4))
end

println("\nNNMF Config:"); print_config(cfg)

data_nnmf = [
(; name=m.name,
status=string(m.status),
time=round(m.time, digits=4),
fe=m.fevals,
ge=m.gevals,
prox = m.proxcalls === missing ? missing : Int(m.proxcalls),
obj = round(m.final_obj, digits=4))
for m in results
]

return data_nnmf
end

# #############################
# # ========= Main ========== #
# #############################

function main(latex_out = false)
function main(;latex_out = false)
data_svm = bench_svm!(CFG)
data_nnmf = bench_nnmf!(CFG2)

all_data = vcat(data_svm, data_nnmf)

println("\n=== Full Benchmark Table ===")
# what is inside the table
for row in all_data
for row in data_svm
println(row)
end

# save as latex format
if latex_out

table_str = pretty_table(String, all_data;
header = ["Method", "Status", L"$t$($s$)", L"$\#f$", L"$\#\nabla f$", L"$\#prox$", "Objective"],
backend = Val(:latex),
alignment = [:l, :c, :r, :r, :r, :r, :r],
)
table_str = pretty_table(String,
data_svm;
backend = :latex,
column_labels = ["Method", "Status", L"$t$($s$)", L"$\#f$", L"$\#\nabla f$", L"$\#prox$", "Objective"],
style = LatexTableStyle(column_label = String[]),
table_format = latex_table_format__booktabs
)

open("Benchmark.tex", "w") do io
write(io, table_str)
Expand Down
3 changes: 0 additions & 3 deletions paper/examples/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,5 @@ MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
ProximalAlgorithms = "140ffc9f-1907-541a-a177-7475e0a401e9"
ProximalCore = "dc4f5ac2-75d1-4f31-931e-60435d74994b"
ProximalOperators = "a725b495-10eb-56fe-b38b-717eba820537"
RegularizedProblems = "ea076b23-609f-44d2-bb12-a4ae45328278"
ShiftedProximalOperators = "d4fd37fa-580c-4e43-9b30-361c21aae263"
1 change: 0 additions & 1 deletion paper/examples/comparison-config.jl
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Expand Up @@ -17,6 +17,5 @@ end

# One global, constant *binding* to a mutable object = type stable & editable
const CFG = Config(QN_FOR_R2N=:LSR1)
const CFG2 = Config(QN_FOR_TR = :LBFGS)

end # module
4 changes: 2 additions & 2 deletions paper/examples/example1.jl → paper/examples/example.jl
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@@ -1,5 +1,5 @@
using LinearAlgebra, Random, ProximalOperators
using NLPModels, RegularizedProblems, RegularizedOptimization
using LinearAlgebra, Random, ShiftedProximalOperators
using NLPModels, NLPModelsModifiers, RegularizedProblems, RegularizedOptimization
using MLDatasets

Random.seed!(1234)
Expand Down