using JLD using JuMP using SCS using GroupRings using PropertyT using ArgParse immutable ProblemData{T} name::String Us::Vector{SparseMatrixCSC{Float64,Int}} Ps::Vector{Array{JuMP.Variable,2}} cnstr::Vector{T} laplacian::SparseVector{Float64} laplacianSq::SparseVector{Float64} dims::Vector{Int} end function sparsify!{T}(U::AbstractArray{T}, eps=eps(T)) # n = rank(U) U[abs.(U) .< eps] = zero(T) # @assert rank(U) == n return sparse(U) end sparsify{T}(U::AbstractArray{T}, eps=eps(T)) = sparsify!(deepcopy(U), eps) small_to_zero!{T}(A::AbstractArray{T}, eps=eps(T)) = A[abs(A) .< eps] = zero(T) function orbit_spvector(vect::AbstractVector, orbits) orb_vector = spzeros(length(orbits)) for (i,o) in enumerate(orbits) k = vect[collect(o)] val = k[1] @assert all(k .== val) orb_vector[i] = val end return orb_vector end function orbit_constraint(cnstrs::Vector{Vector{Vector{Int64}}}, n) result = spzeros(n,n) for cnstr in cnstrs for p in cnstr result[p[1],p[2]] += 1.0 end end return 1/length(cnstrs)*result 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, P[k][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 init_ProblemData(name::String) splap = load(joinpath(name, "delta.jld"), "delta"); pm = load(joinpath(name, "pm.jld"), "pm"); cnstr = PropertyT.constraints_from_pm(pm); splap² = GroupRings.mul(splap, splap, pm); Uπs = load(joinpath(name, "U_pis.jld"), "Uπs"); #dimensions of the corresponding πs: dims = [1,1,2,3,3,4,4,8,6,6,6,6,4,4,8,1,1,2,3,3] # dims load(joinpath(name, "U_pis.jld"), "dims") Uπs = sparsify.(Uπs); 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) orb_SOutF4data = ProblemData(name, Uπs, P, orb_spcnstrm, orb_splap, orb_splap², dims); return m, orb_SOutF4data end function transform{T}(U::AbstractArray{T,2}, V::AbstractArray{T,2}, eps=eps(T)) w = U'*V*U sparsify!(w, eps) dropzeros!(w) return w end A(data::ProblemData, π, t) = data.dims[π]*transform(data.Us[π], data.cnstr[t]) function constrLHS(m::JuMP.Model, data::ProblemData, 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 addconstraints!(m::JuMP.Model, data::ProblemData, l::Int=length(data.cnstr); var::Symbol = :λ) λ = getvariable(m, var) for t in 1:l d, d² = data.laplacian[t], data.laplacianSq[t] lhs = constrLHS(m, data, t) if lhs == zero(lhs) if d == 0 && d² == 0 info("Detected empty constraint") continue else warn("Adding unsatisfiable constraint!") end end JuMP.@constraint(m, lhs == d² - λ*d) end end function reconstructP(m::JuMP.Model, data::ProblemData) computedPs = [getvalue(P) for P in data.Ps] return sum(data.dims[π]*data.Us[π]*computedPs[π]*data.Us[π]' for π in 1:endof(data.Ps)) end function cpuinfo_physicalcores() maxcore = -1 for line in eachline("/proc/cpuinfo") if startswith(line, "core id") maxcore = max(maxcore, parse(Int, split(line, ':')[2])) end end maxcore < 0 && error("failure to read core ids from /proc/cpuinfo") return maxcore + 1 end function parse_commandline() s = ArgParseSettings() @add_arg_table s begin "--tol" help = "set numerical tolerance for the SDP solver (default: 1e-5)" arg_type = Float64 default = 1e-5 "--iterations" help = "set maximal number of iterations for the SDP solver (default: 20000)" arg_type = Int default = 20000 "--upper-bound" help = "Set an upper bound for the spectral gap (default: Inf)" arg_type = Float64 default = Inf "--cpus" help = "Set number of cpus used by solver (default: auto)" arg_type = Int required = false # "-N" # help = "Consider automorphisms of free group on N generators (default: N=3)" # arg_type = Int # default = 2 end return parse_args(s) end function create_SDP_problem(name::String; upper_bound=Inf) info(PropertyT.logger, "Loading data....") t = @timed SDP_problem, orb_data = init_ProblemData(name); info(PropertyT.logger, PropertyT.timed_msg(t)) if upper_bound < Inf λ = JuMP.getvariable(SDP_problem, :λ) 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::ProblemData) info(PropertyT.logger, "Solving SDP problem...") varλ = JuMP.getvariable(m, :λ) varP = data.Ps λ, P = PropertyT.λandP(data.name, m, varλ, varP) recP = reconstructP(m, data) fname = PropertyT.λSDPfilenames(data.name)[2] save(fname, "origP", P, "P", recP) return λ, recP end function main() name = "SOutF4_E4" if !isdir(name) throw("Create dir with all the required orbit data first!") end logger = PropertyT.setup_logging(name) parsed_args = parse_commandline() if parsed_args["cpus"] != nothing if parsed_args["cpus"] > cpuinfo_physicalcores() warn("Number of specified cores exceeds the physical core cound. Performance will suffer.") end Blas.set_num_threads(parsed_args["cpus"]) end tol = parsed_args["tol"] iterations = parsed_args["iterations"] upper_bound = parsed_args["upper-bound"] solver = SCS.SCSSolver(eps=tol, max_iters=iterations, verbose=true, linearsolver=SCS.Indirect) fnames = PropertyT.λSDPfilenames(name) if all(isfile.(fnames)) && false λ, P = PropertyT.λandP(name) else info(logger, "Creating SDP problem...") SDP_problem, orb_data = create_SDP_problem(name, upper_bound=upper_bound) JuMP.setsolver(SDP_problem, solver) λ, P = λandP(SDP_problem, orb_data) end info(PropertyT.logger, "λ = $λ") info(PropertyT.logger, "sum(P) = $(sum(P))") info(PropertyT.logger, "maximum(P) = $(maximum(P))") info(PropertyT.logger, "minimum(P) = $(minimum(P))") end main()