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shorten the OptimizationMadNLP tests #1082

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Oct 24, 2025
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shorten the OptimizationMadNLP tests #1082

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d80cc3b
shorten the OptimizationMadNLP tests
SebastianM-C Oct 24, 2025
560e76a
more verbose
SebastianM-C Oct 24, 2025
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32 changes: 12 additions & 20 deletions lib/OptimizationMadNLP/test/runtests.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -322,7 +322,7 @@ end
end
end

@testset "LBFGS Hessian Approximation" begin
@testset verbose=true "LBFGS Hessian Approximation" begin
# Based on https://madsuite.org/MadNLP.jl/dev/tutorials/lbfgs/

@testset "Unconstrained LBFGS" begin
Expand Down Expand Up @@ -383,7 +383,7 @@ end
end
end

@testset "Constrained LBFGS - Electrons on Sphere" begin
@testset verbose=true "Constrained LBFGS - Electrons on Sphere" begin
# Quasi-uniform distribution of electrons on a unit sphere
# Minimize electrostatic potential energy (Coulomb potential)
# Variables are organized as [x1, x2, ..., xn, y1, y2, ..., yn, z1, z2, ..., zn]
Expand Down Expand Up @@ -436,9 +436,8 @@ end
return x0
end

@testset "N=$np electrons with $approx" for np in [6, 8, 10],
approx in [MadNLP.CompactLBFGS, MadNLP.ExactHessian]

@testset "N=5 electrons with $approx" for approx in [MadNLP.CompactLBFGS, MadNLP.ExactHessian]
np = 5
x0 = init_electrons_on_sphere(np)

if approx == MadNLP.CompactLBFGS
Expand Down Expand Up @@ -477,18 +476,11 @@ end
unit_sphere_constraints(cons_vals, sol.u, nothing)
@test all(abs.(cons_vals) .< 1e-5)

# Known optimal energies for small electron numbers
# Known optimal energy for 5 electrons on unit sphere
# Reference: https://en.wikipedia.org/wiki/Thomson_problem
# Note: These are the minimum Coulomb potential energies for N electrons on unit sphere
expected_energies = Dict(
6 => 9.985281374, # Octahedron (Oh symmetry)
8 => 19.675287861, # Square antiprism (D4d)
10 => 32.716949460 # Gyroelongated square dipyramid (D4d)
)

if haskey(expected_energies, np)
@test isapprox(sol.objective, expected_energies[np], rtol = 1e-3)
end
# Configuration: Triangular dipyramid (trigonal bipyramid, D3h symmetry)
expected_energy = 6.474691495
@test isapprox(sol.objective, expected_energy, rtol = 1e-3)

# Verify minimum distance between electrons
x = sol.u[1:np]
Expand All @@ -507,7 +499,7 @@ end

@testset "LBFGS vs Exact Hessian" begin
# Test with moderate size to show LBFGS efficiency
np = 12 # Icosahedron configuration
np = 10 # Gyroelongated square dipyramid configuration
x0 = init_electrons_on_sphere(np)

results = []
Expand All @@ -517,7 +509,7 @@ end
("ExactHessian",
MadNLP.ExactHessian,
SecondOrder(
AutoForwardDiff(), AutoForwardDiff()))]
AutoForwardDiff(), AutoZygote()))]
optfunc = OptimizationFunction(
coulomb_potential, ad,
cons = unit_sphere_constraints
Expand All @@ -543,11 +535,11 @@ end
# All methods should converge
@test all(r[2].success for r in values(results))

# All should find similar objective values (icosahedron energy)
# All should find similar objective values (gyroelongated square dipyramid energy)
# Reference: https://en.wikipedia.org/wiki/Thomson_problem
objectives = [r[2].objective for r in values(results)]
@testset "$(results[i][1])" for (i, o) in enumerate(objectives)
@test o ≈ 49.165253058 rtol=1e-2
@test o ≈ 32.716949460 rtol=1e-2
end

# LBFGS methods typically need more iterations but less cost per iteration
Expand Down
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