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[GeneralDichotomy] handle no solution case #207

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Merged
odow merged 6 commits into from
Jul 2, 2026
Merged

[GeneralDichotomy] handle no solution case #207

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d7d30c7
handle no solution case
Jul 1, 2026
d586717
test all initial weights in GeneralDichotomy to find the initial solu…
Jul 2, 2026
997ba8c
Update ext/Polyhedra/GeneralDichotomy.jl
smbct Jul 2, 2026
8990d1f
Update ext/Polyhedra/GeneralDichotomy.jl
smbct Jul 2, 2026
b45f362
Merge branch 'master' into general_dichotomy_203
odow Jul 2, 2026
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Update
odow Jul 2, 2026
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10 changes: 4 additions & 6 deletions ext/Polyhedra/GeneralDichotomy.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -24,9 +24,8 @@ function MOA.minimize_multiobjective!(
# Storage we need for the algorithm.
weights, solutions = Weight[], MOA.SolutionPoint[]
n_obj = MOI.output_dimension(model.f)
# First, minimize the first objective to obtain a primal feasible point.
w = zeros(Float64, n_obj)
w[1] = 1.0
# First, minimize the combined objectives to obtain a primal feasible point.
w = ones(Float64, n_obj)

@odow odow Jul 1, 2026

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Suggested change
w = ones(Float64, n_obj)
w = fill(1 / n_obj, n_obj)

Does it matter?

@smbct smbct Jul 2, 2026

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At this stage, this weight is not kept to be part of the "decomposition", so its range does not matter here. (1,..,1) is unlikely to ba an "extreme weight" since extreme weights verify w dot y^1 = w dot y^2 = ... (usually there is equality for d solutions if there are d objectives). This is really the same as the dichotomy method.

In the main loop, all extreme weights are obtained from the Polyhedron vertices enumerations, so they verify both the range and the equality. (and initial weights e.g. (1,0,..,0) verify this as well when there is one recorded solution)

status, solution = MOA._solve_weighted_sum(model, alg, w)
if solution === nothing
return status, nothing
Expand All @@ -40,7 +39,7 @@ function MOA.minimize_multiobjective!(
w[i] = 1.0
z = w' * solution.y
adj_bnd = Int[-j for j in 1:n_obj if j != i]
push!(weights, Weight(w, z, adj_bnd, [1], i == 1, false))
push!(weights, Weight(w, z, adj_bnd, [1], false, false))
end
# Prevent solution duplicates: existing_sol maps an rounded objective vector
# to its index in `solutions::Vector{MOA.SolutionPoint}`.
Expand All @@ -59,8 +58,7 @@ function MOA.minimize_multiobjective!(
end
status, sol = MOA._solve_weighted_sum(model, alg, weight.w)
if sol === nothing
# TODO(odow): what to do when this solve fails?
return status, solutions
return MOI.NUMERICAL_ERROR, solutions
end
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weight.tested = true
if !haskey(existing_sol, _round(sol.y; atol))
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