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move to using sparse matrices in symmetrized sdp
dense are faster for small sizes only
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@ -216,15 +216,14 @@ function sos_problem_primal(
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P = map(direct_summands(wedderburn)) do ds
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dim = size(ds, 1)
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P = JuMP.@variable(model, [1:dim, 1:dim], Symmetric)
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@constraint(model, P in PSDCone())
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JuMP.@constraint(model, P in PSDCone())
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P
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end
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begin # preallocating
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T = eltype(wedderburn)
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M = zeros.(T, size.(P))
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M = spzeros.(T, size.(P))
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M_orb = zeros(T, size(parent(elt).mstructure)...)
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tmps = SymbolicWedderburn._tmps(wedderburn)
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end
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X = convert(Vector{T}, StarAlgebras.coeffs(elt))
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@ -235,16 +234,27 @@ function sos_problem_primal(
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@info "Adding $(length(invariant_vectors(wedderburn))) constraints"
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for iv in invariant_vectors(wedderburn)
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ds = SymbolicWedderburn.direct_summands(wedderburn)
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Uπs = SymbolicWedderburn.image_basis.(ds)
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T = eltype(first(Uπs))
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degrees = SymbolicWedderburn.degree.(ds)
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for (i, iv) in enumerate(invariant_vectors(wedderburn))
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# @debug i
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i % 100 == 0 && print('.')
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i % 10000 === 0 && print('\n')
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x = dot(X, iv)
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u = dot(U, iv)
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M_orb = invariant_constraint!(M_orb, basis(parent(elt)), cnstrs, iv)
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M = SymbolicWedderburn.diagonalize!(M, M_orb, Uπs, degrees)
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SparseArrays.droptol!.(M, 10 * eps(T) * max(size(M_orb)...))
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M = SymbolicWedderburn.diagonalize!(M, M_orb, wedderburn, tmps)
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SymbolicWedderburn.zerotol!.(M, atol=1e-12)
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M_dot_P = sum(dot(M[π], P[π]) for π in eachindex(M) if !iszero(M[π]))
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# @debug [nnz(m) / length(m) for m in M]
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# spM = sparse.(M)
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# @time M_dot_P = sum(dot(spM[π], P[π]) for π in eachindex(spM) if !iszero(spM[π]))
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# @info density = [count(!iszero, m) / sum(length, m) for m in M]
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if feasibility_problem
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JuMP.@constraint(model, x == __fast_recursive_dot!(JuMP.AffExpr(), P, M))
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