1
0
mirror of https://github.com/kalmarek/PropertyT.jl.git synced 2024-11-25 00:40:28 +01:00

add cocycle constraints from SDP duality paper by M.Nitsche

This commit is contained in:
Marek Kaluba 2024-02-06 13:34:10 +01:00
parent acb6b08ba9
commit b01e26104e
No known key found for this signature in database
GPG Key ID: 8BF1A3855328FC15

View File

@ -36,6 +36,31 @@ function sos_problem_dual(
return model
end
function geometric_constraints!(
model::JuMP.Model,
elt::StarAlgebras.AlgebraElement,
)
A = parent(elt)
G = parent(A)
mstr = A.mstructure
b = basis(A)
y = model[:y]
for g in gens(G)
for h in gens(G)
gh = mstr[b[g], b[h]]
if elt[gh] > 0
for γ in axes(mstr, 1)
γgh = mstr[γ, gh]
γg = mstr[γ, b[g]]
γh = mstr[γ, b[h]]
JuMP.@constraint model y[γgh] + y[γ] == y[γg] + y[γh]
end
end
end
end
return model
end
function decompose(
elt::StarAlgebras.AlgebraElement,
wd::WedderburnDecomposition,
@ -70,6 +95,29 @@ function decompose(v::AbstractVector, invariant_vecs)
return res, norm(current - v)
end
function _dot(elt::StarAlgebras.AlgebraElement, Y, wd::WedderburnDecomposition)
inv_vecs = invariant_vectors(wd)
v = StarAlgebras.coeffs(elt)
res, error = _dot(v, Y, inv_vecs)
_eps = length(v) * eps(typeof(error))
error < _eps || @warn "elt does not seem to be invariant" error
return res
end
function _dot(v::AbstractVector, Y::AbstractVector{<:JuMP.AbstractVariableRef})
@assert length(inv_vecs) == length(Y)
@assert length(v) == length(first(inv_vecs))
res = JuMP.AffExpr()
cfs, error = decompose(v, inv_vecs)
for i in SparseArrays.nonzeroinds(cfs)
(c, y) = cfs[i], Y[i]
JuMP.add_to_expression!(res, c, y)
end
return res, error
end
function sos_problem_dual(
elt::StarAlgebras.AlgebraElement,
order_unit::StarAlgebras.AlgebraElement,
@ -123,6 +171,44 @@ function sos_problem_dual(
return model, Ps
end
function __find_firstnz(i, inv_vecs)
for (idx, iv) in enumerate(inv_vecs)
iv[i] 0 && return idx
end
return nothing
end
function geometric_constraints!(
model::JuMP.Model,
elt::StarAlgebras.AlgebraElement,
wd::WedderburnDecomposition,
)
A = parent(elt)
G = parent(A)
mstr = A.mstructure
b = basis(A)
y = model[:y_orb]
# cfs = PropertyT.decompose(elt, wd)
inv_vecs = invariant_vectors(wd)
for g in gens(G)
for h in gens(G)
gh = mstr[b[g], b[h]]
if elt[gh] > 0
for (γ, iv) in enumerate(inv_vecs)
γ_basis_idx = first(SparseArrays.nonzeroinds(iv))
γ_basis_idx > size(mstr, 1) && break
γgh = __find_firstnz(mstr[γ_basis_idx, gh], inv_vecs)
γg = __find_firstnz(mstr[γ_basis_idx, b[g]], inv_vecs)
γh = __find_firstnz(mstr[γ_basis_idx, b[h]], inv_vecs)
JuMP.@constraint model y[γgh] + y[γ] == y[γg] + y[γh]
end
end
end
end
end
"""
sos_problem_primal(X, [u = zero(X); upper_bound=Inf])
Formulate sum of squares decomposition problem for `X - λ·u`.
@ -147,10 +233,11 @@ function sos_problem_primal(
upper_bound = Inf,
augmented::Bool = iszero(StarAlgebras.aug(elt)) &&
iszero(StarAlgebras.aug(order_unit)),
support = 1:size(parent(elt).mstructure, 1),
)
@assert parent(elt) === parent(order_unit)
N = LinearAlgebra.checksquare(parent(elt).mstructure)
N = length(support)
model = JuMP.Model()
P = JuMP.@variable(model, P[1:N, 1:N], Symmetric)
JuMP.@constraint(model, psd, P in PSDCone())
@ -159,11 +246,11 @@ function sos_problem_primal(
@warn "Setting `upper_bound` together with zero `order_unit` has no effect"
end
A = constraints(parent(elt); augmented = augmented)
A = constraints(parent(elt), support; augmented = augmented)
if !iszero(order_unit)
λ = JuMP.@variable(model, λ)
if isfinite(upper_bound)
if !isfinite(upper_bound)
JuMP.@constraint model λ <= upper_bound
end
JuMP.@objective(model, Max, λ)
@ -282,9 +369,9 @@ function sos_problem_primal(
end
end
id_one = findfirst(invariant_vectors(wedderburn)) do v
b = basis(parent(elt))
return sparsevec([b[one(first(b))]], [1 // 1], length(v)) == v
id_one = let b = basis(parent(elt)), iv = invariant_vectors(wedderburn)
id_v = sparsevec([b[one(first(b))]], [1 // 1], length(first(iv)))
findfirst(==(id_v), iv)
end
prog = ProgressMeter.Progress(
@ -303,12 +390,12 @@ function sos_problem_primal(
if !feasibility_problem # add λ or not?
λ = JuMP.@variable(model, λ)
JuMP.@objective(model, Max, λ)
if isfinite(upper_bound)
JuMP.@constraint(model, λ <= upper_bound)
if feasibility_problem
@warn "setting `upper_bound` with zero `orderunit` has no effect"
end
end
if isfinite(upper_bound)
if feasibility_problem
@warn "setting `upper_bound` with zero `orderunit` has no effect"
else
JuMP.@constraint(model, ub, λ <= upper_bound)
end
end