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Merged
merged 6 commits into from
Dec 13, 2024
Merged

add safety check for adaptive #534

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1226ab0
add safety check for adaptive
matbesancon Dec 11, 2024
8c287e2
SCIP in test deps
matbesancon Dec 12, 2024
47e1072
Revert "SCIP in test deps"
matbesancon Dec 12, 2024
589bef1
SCIP to HiGHS
matbesancon Dec 12, 2024
2f0699f
Added failing instance as a test.
Dec 12, 2024
6a10e11
Merge changes to repository
Dec 12, 2024
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27 changes: 22 additions & 5 deletions examples/optimal_experiment_design.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -4,11 +4,12 @@ using Random
using Distributions
using LinearAlgebra
using Statistics
using SCIP
using HiGHS
using MathOptInterface
const MOI = MathOptInterface
using SparseArrays
using Test
using StableRNGs

# The Optimal Experiment Design Problem consists of choosing a subset of experiments
# maximising the information gain.
Expand All @@ -34,7 +35,10 @@ seed - for the Random functions.
m - number of experiments.
Build the experiment matrix A.
"""
function build_data(m)
function build_data(m; seed=nothing)
if seed != nothing
rng = StableRNG(seed)
end
n = Int(floor(m/10))
B = rand(m,n)
B = B'*B
Expand All @@ -51,7 +55,7 @@ end
Build MOI version of the lmo.
"""
function build_moi_lmo(m)
o = SCIP.Optimizer()
o = HiGHS.Optimizer()
MOI.empty!(o)
MOI.set(o, MOI.Silent(), true)

Expand Down Expand Up @@ -267,7 +271,7 @@ m = 300
x_b, _, primal, dual_gap, traj_data_b, _ = FrankWolfe.blended_conditional_gradient(f, grad!, lmo, x0, verbose=true, trajectory=true,line_search=FrankWolfe.Secant(domain_oracle=domain_oracle))

@test traj_data_s[end][1] < traj_data[end][1]
@test traj_data_d[end][1] <= traj_data_s[end][1]
@test traj_data_d[end][1] <= traj_data[end][1]
@test traj_data_b[end][1] <= traj_data_s[end][1]
@test isapprox(f(x_s), f(x))
@test isapprox(f(x_s), f(x_d))
Expand Down Expand Up @@ -302,11 +306,24 @@ m = 300
x_b, _, primal, dual_gap, traj_data_b, _ = FrankWolfe.blended_conditional_gradient(f, grad!, lmo, x0, verbose=true, trajectory=true,line_search=FrankWolfe.Secant(domain_oracle=domain_oracle))

@test traj_data_s[end][1] < traj_data[end][1]
@test traj_data_d[end][1] <= traj_data_s[end][1]
@test traj_data_d[end][1] <= traj_data[end][1]
@test traj_data_b[end][1] <= traj_data_s[end][1]
@test isapprox(f(x_s), f(x))
@test isapprox(f(x_s), f(x_d))
@test isapprox(f(x_s), f(x_b))
end
end

@testset "Failing Optimal Design Instance with AFW" begin
seed = 8775360557774874450
m = 100
A = build_data(m, seed=seed)
f, grad! = build_a_criterion(A)

lmo = FrankWolfe.ProbabilitySimplexOracle(1.0)
x0, active_set, _ = build_start_point(A)

_, _, primal, dual_gap, _, _ = FrankWolfe.away_frank_wolfe(f, grad!, lmo, active_set; verbose=true)
@test isfinite(primal)
end

3 changes: 2 additions & 1 deletion src/linesearch.jl
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Expand Up @@ -709,9 +709,10 @@ function perform_line_search(
niter = 0
clipping = false
gradient_storage = storage.gradient_storage
f_x = f(x)

grad!(gradient_storage, x_storage)
while 0 > fast_dot(gradient_storage, d) && γ ≥ 100 * eps(float(γ))
while f(x_storage) > f_x || 0 > fast_dot(gradient_storage, d) && γ ≥ 100 * eps(float(γ))
M *= line_search.tau
γ = min(max(dot_dir / (M * ndir2), 0), gamma_max)
x_storage = muladd_memory_mode(memory_mode, x_storage, x, γ, d)
Expand Down
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