PropertyT.jl/src/Orbit-wise.jl

232 lines
6.4 KiB
Julia

using JuMP
using SCS
export Settings, OrbitData
immutable Settings
name::String
N::Int
G::Group
S::Vector
AutS::Group
radius::Int
solver::SCSSolver
upper_bound::Float64
tol::Float64
end
immutable OrbitData
name::String
Us::Vector
Ps::Vector{Array{JuMP.Variable,2}}
cnstr::Vector
laplacian::Vector
laplacianSq::Vector
dims::Vector{Int}
end
function OrbitData(name::String)
splap = load(joinpath(name, "delta.jld"), "Δ");
pm = load(joinpath(name, "pm.jld"), "pm");
cnstr = PropertyT.constraints_from_pm(pm);
splap² = similar(splap)
splap² = GroupRings.mul!(splap², splap, splap, pm);
# Uπs = load(joinpath(name, "U_pis.jld"), "Uπs");
Uπs = load(joinpath(name, "U_pis.jld"), "spUπs");
#dimensions of the corresponding πs:
dims = load(joinpath(name, "U_pis.jld"), "dims")
m, P = init_model(Uπs);
orbits = load(joinpath(name, "orbits.jld"), "orbits");
n = size(Uπs[1],1)
orb_spcnstrm = [orbit_constraint(cnstr[collect(orb)], n) for orb in orbits]
orb_splap = orbit_spvector(splap, orbits)
orb_splap² = orbit_spvector(splap², orbits)
orbData = OrbitData(name, Uπs, P, orb_spcnstrm, orb_splap, orb_splap², dims);
# orbData = OrbitData(name, Uπs, P, orb_spcnstrm, splap, splap², dims);
return m, orbData
end
include("OrbitDecomposition.jl")
dens(M::SparseMatrixCSC) = length(M.nzval)/length(M)
dens(M::AbstractArray) = sum(abs.(M) .!= 0)/length(M)
function sparsify{T}(U::AbstractArray{T}, check=true)
W = deepcopy(U)
W[abs.(W) .< eps(T)] .= zero(T)
if check && rank(W) != rank(U)
info("Sparsification would decrease the rank!")
W = U
else
info("Sparsified:", rpad(dens(U), 10), "\t", rpad(dens(W),10))
end
W = sparse(W)
dropzeros!(W)
return W
end
function init_orbit_data(logger, sett::Settings; radius=2)
ex(fname) = isfile(joinpath(sett.name, fname))
files_exists = ex.(["delta.jld", "pm.jld", "U_pis.jld", "orbits.jld"])
if !all(files_exists)
compute_orbit_data(logger, sett.name, sett.G, sett.S, sett.AutS, radius=radius)
end
return 0
end
function transform(U::AbstractArray, V::AbstractArray; sparse=true)
if sparse
return sparsify(U'*V*U)
else
return U'*V*U
end
end
A(data::OrbitData, π, t) = data.dims[π].*transform(data.Us[π], data.cnstr[t])
function constrLHS(m::JuMP.Model, data::OrbitData, t)
l = endof(data.Us)
lhs = @expression(m, sum(vecdot(A(data, π, t), data.Ps[π]) for π in 1:l))
return lhs
end
function constrLHS(m::JuMP.Model, cnstr, Us, Ust, dims, vars)
lhs = @expression(m,
sum(vecdot(dims[π].*Ust[π]*cnstr*Us[π], vars[π]) for π in 1:endof(Us)))
return lhs
end
function addconstraints!(m::JuMP.Model, data::OrbitData, l::Int=length(data.laplacian); var::Symbol = )
λ = m[var]
Ust = [U' for U in data.Us]
for t in 1:l
# lhs = constrLHS(m, data, t)
lhs = constrLHS(m, data.cnstr[t], data.Us, Ust, data.dims, data.Ps)
d, = data.laplacian[t], data.laplacianSq[t]
if lhs == zero(lhs)
if d == 0 && == 0
info("Detected empty constraint")
continue
else
warn("Adding unsatisfiable constraint!")
end
end
JuMP.@constraint(m, lhs == - λ*d)
end
end
function init_model(Uπs)
m = JuMP.Model();
l = size(Uπs,1)
P = Vector{Array{JuMP.Variable,2}}(l)
for k in 1:l
s = size(Uπs[k],2)
P[k] = JuMP.@variable(m, [i=1:s, j=1:s])
JuMP.@SDconstraint(m, P[k] >= 0.0)
end
JuMP.@variable(m, λ >= 0.0)
JuMP.@objective(m, Max, λ)
return m, P
end
function create_SDP_problem(name::String; upper_bound=Inf)
info(PropertyT.logger, "Loading orbit data....")
t = @timed SDP_problem, orb_data = OrbitData(name);
info(PropertyT.logger, PropertyT.timed_msg(t))
if upper_bound < Inf
λ = JuMP.getvariable(SDP_problem, )
JuMP.@constraint(SDP_problem, λ <= upper_bound)
end
info(PropertyT.logger, "Adding constraints... ")
t = @timed addconstraints!(SDP_problem, orb_data)
info(PropertyT.logger, PropertyT.timed_msg(t))
return SDP_problem, orb_data
end
function λandP(m::JuMP.Model, data::OrbitData)
varλ = m[]
varP = data.Ps
λ, Ps = PropertyT.λandP(data.name, m, varλ, varP)
return λ, Ps
end
function λandP(m::JuMP.Model, data::OrbitData, sett::Settings)
info(PropertyT.logger, "Solving SDP problem...")
λ, Ps = λandP(m, data)
info(PropertyT.logger, "Reconstructing P...")
@time mreps = matrix_reps(sett.G, sett.S, sett.AutS, sett.radius)
@time recP = reconstruct_sol(mreps, data.Us, Ps, data.dims)
fname = PropertyT.λSDPfilenames(data.name)[2]
save(fname, "origP", Ps, "P", recP)
return λ, recP
end
function check_property_T(sett::Settings)
init_orbit_data(logger, sett, radius=sett.radius)
Δ = PropertyT.ΔandSDPconstraints(sett.name, sett.G)[1]
fnames = PropertyT.λSDPfilenames(sett.name)
if all(isfile.(fnames))
λ, P = PropertyT.λandP(sett.name)
else
info(logger, "Creating SDP problem...")
SDP_problem, orb_data = create_SDP_problem(sett.name, upper_bound=sett.upper_bound)
JuMP.setsolver(SDP_problem, sett.solver)
λ, P = λandP(SDP_problem, orb_data, sett)
end
info(logger, "λ = ")
info(logger, "sum(P) = $(sum(P))")
info(logger, "maximum(P) = $(maximum(P))")
info(logger, "minimum(P) = $(minimum(P))")
if λ > 0
isapprox(eigvals(P), abs.(eigvals(P)), atol=sett.tol) ||
warn("The solution matrix doesn't seem to be positive definite!")
# @assert P == Symmetric(P)
Q = real(sqrtm(Symmetric(P)))
sgap = PropertyT.check_distance_to_positive_cone(Δ, λ, Q, 2*sett.radius, tol=sett.tol, rational=false)
if isa(sgap, Interval)
sgap = sgap.lo
end
if sgap > 0
info(logger, "λ ≥ $(Float64(trunc(sgap,12)))")
Kazhdan_κ = PropertyT.Kazhdan_from_sgap(sgap, length(sett.S))
Kazhdan_κ = Float64(trunc(Kazhdan_κ, 12))
info(logger, "κ($(sett.name), S) ≥ $Kazhdan_κ: Group HAS property (T)!")
return true
else
sgap = Float64(trunc(sgap, 12))
info(logger, "λ($(sett.name), S) ≥ $sgap: Group may NOT HAVE property (T)!")
return false
end
end
info(logger, "κ($(sett.name), S) ≥ < 0: Tells us nothing about property (T)")
return false
end