kalmar/PropertyT.jl
1
0
Fork 0
mirror of https://github.com/kalmarek/PropertyT.jl.git synced 2024-11-19 07:20:28 +01:00
Code Issues Releases Wiki Activity

move to using sparse matrices in symmetrized sdp

Browse Source 
dense are faster for small sizes only
This commit is contained in:
Marek Kaluba 2022-11-14 19:50:09 +01:00
parent 2f89538eb0
commit 971e07b819
No known key found for this signature in database
GPG Key ID: 8BF1A3855328FC15
1 changed files with 18 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

26
src/sos_sdps.jl
View File

@ -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))