mirror of
https://github.com/kalmarek/PropertyT.jl.git
synced 2024-11-26 00:55:27 +01:00
move to using sparse matrices in symmetrized sdp
dense are faster for small sizes only
This commit is contained in:
parent
2f89538eb0
commit
971e07b819
@ -216,15 +216,14 @@ function sos_problem_primal(
|
||||
P = map(direct_summands(wedderburn)) do ds
|
||||
dim = size(ds, 1)
|
||||
P = JuMP.@variable(model, [1:dim, 1:dim], Symmetric)
|
||||
@constraint(model, P in PSDCone())
|
||||
JuMP.@constraint(model, P in PSDCone())
|
||||
P
|
||||
end
|
||||
|
||||
begin # preallocating
|
||||
T = eltype(wedderburn)
|
||||
M = zeros.(T, size.(P))
|
||||
M = spzeros.(T, size.(P))
|
||||
M_orb = zeros(T, size(parent(elt).mstructure)...)
|
||||
tmps = SymbolicWedderburn._tmps(wedderburn)
|
||||
end
|
||||
|
||||
X = convert(Vector{T}, StarAlgebras.coeffs(elt))
|
||||
@ -235,16 +234,27 @@ function sos_problem_primal(
|
||||
|
||||
@info "Adding $(length(invariant_vectors(wedderburn))) constraints"
|
||||
|
||||
for iv in invariant_vectors(wedderburn)
|
||||
ds = SymbolicWedderburn.direct_summands(wedderburn)
|
||||
Uπs = SymbolicWedderburn.image_basis.(ds)
|
||||
T = eltype(first(Uπs))
|
||||
degrees = SymbolicWedderburn.degree.(ds)
|
||||
|
||||
for (i, iv) in enumerate(invariant_vectors(wedderburn))
|
||||
# @debug i
|
||||
i % 100 == 0 && print('.')
|
||||
i % 10000 === 0 && print('\n')
|
||||
|
||||
x = dot(X, iv)
|
||||
u = dot(U, iv)
|
||||
|
||||
M_orb = invariant_constraint!(M_orb, basis(parent(elt)), cnstrs, iv)
|
||||
M = SymbolicWedderburn.diagonalize!(M, M_orb, Uπs, degrees)
|
||||
SparseArrays.droptol!.(M, 10 * eps(T) * max(size(M_orb)...))
|
||||
|
||||
M = SymbolicWedderburn.diagonalize!(M, M_orb, wedderburn, tmps)
|
||||
SymbolicWedderburn.zerotol!.(M, atol=1e-12)
|
||||
|
||||
M_dot_P = sum(dot(M[π], P[π]) for π in eachindex(M) if !iszero(M[π]))
|
||||
# @debug [nnz(m) / length(m) for m in M]
|
||||
# spM = sparse.(M)
|
||||
# @time M_dot_P = sum(dot(spM[π], P[π]) for π in eachindex(spM) if !iszero(spM[π]))
|
||||
# @info density = [count(!iszero, m) / sum(length, m) for m in M]
|
||||
|
||||
if feasibility_problem
|
||||
JuMP.@constraint(model, x == __fast_recursive_dot!(JuMP.AffExpr(), P, M))
|
||||
|
Loading…
Reference in New Issue
Block a user