KSparseBallLMO#268
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… situations, so further improvements are needed.
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## main #268 +/- ##
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- Coverage 86.23% 82.53% -3.71%
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There is only DiamondLMO(L1) and KsparseLMO |
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Now is only Ksparse |
| struct KSparseBLMO <: FrankWolfe.LinearMinimizationOracle | ||
| K::Int | ||
| right_hand_side::Float64 | ||
| end |
| function bounded_compute_extreme_point( | ||
| lmo::KSparseBLMO, | ||
| direction, | ||
| lb, | ||
| ub, | ||
| int_vars; | ||
| v=nothing, | ||
| kwargs..., | ||
| ) |
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function bounded_compute_extreme_point(
lmo::FrankWolfe.KSparseLMO{T},
direction,
lb,
ub,
int_vars;
v=nothing,
kwargs...
)
..
end
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| n = 20 | ||
| sparsity = 0.3 | ||
| x_sol = [rand() < sparsity ? 0 : rand(1:floor(Int, n / 4)) for _ in 1:n] | ||
| n_int = 17 |
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I would rename the file to "custom_lmo_testing.jl".
| using FrankWolfe | ||
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| @testset "LMO vs MOI on the same feasible region" begin |
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Specify the feasible region as we are going to add tests for all custom LMOs.
| # --- MOI --- | ||
| model = MOI.instantiate(HiGHS.Optimizer) | ||
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I think the model would be easier to read if you use JuMP. MOI is essentially the back-end of JuMP.
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| v_moi = [MOI.get(model, MOI.VariablePrimal(), v[i]) for i in 1:n] | ||
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| # compare objective values (not pointwise equality!) |
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You do want to verify that you get the same point. This should check entry-wise equality.
2 participants
1.Extend the Polytope LMOs from FW to Boscia: KNormBallLMO, DiamondLMO (generalisation of the L1-norm ball), KSparseLMO
2.For the DiamondLMO, the L1 norm version is the only implemented,a diamond constraint can be inputed, but the result will most likely be incorrect because it implements L1 norm. haven't figured out how to implement a diamond constraint yet.