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https://github.com/kalmarek/PropertyT.jl.git
synced 2024-11-26 00:55:27 +01:00
fix: deprecations for julia-0.7/1.0
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@ -5,11 +5,13 @@ version = "0.1.0"
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[deps]
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AbstractAlgebra = "c3fe647b-3220-5bb0-a1ea-a7954cac585d"
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Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
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GroupRings = "0befed6a-bd73-11e8-1e41-a1190947c2f5"
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Groups = "5d8bd718-bd84-11e8-3b40-ad14f4a32557"
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IntervalArithmetic = "d1acc4aa-44c8-5952-acd4-ba5d80a2a253"
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JLD = "4138dd39-2aa7-5051-a626-17a0bb65d9c8"
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JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
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MathProgBase = "fdba3010-5040-5b88-9595-932c9decdf73"
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Nemo = "2edaba10-b0f1-5616-af89-8c11ac63239a"
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@ -1,3 +1,5 @@
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using Printf
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###############################################################################
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#
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# Settings and filenames
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@ -84,14 +86,15 @@ end
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function computeλandP(sett::Naive, Δ::GroupRingElem;
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solverlog=tempname()*".log")
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info("Creating SDP problem...")
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info(Base.repr(SDP_problem))
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@info("Creating SDP problem...")
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SDP_problem, varλ, varP = SOS_problem(Δ^2, Δ, upper_bound=sett.upper_bound)
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JuMP.setsolver(SDP_problem, sett.solver)
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@info(Base.repr(SDP_problem))
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ws = warmstart(sett)
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@time status, (λ, P, ws) = PropertyT.solve(solverlog, SDP_problem, varλ, varP, ws)
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@show status
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@info("Solver's status: $status")
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save(filename(sett, :warmstart), "warmstart", ws, "P", P, "λ", λ)
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return λ, P
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@ -108,17 +111,17 @@ function computeλandP(sett::Symmetrized, Δ::GroupRingElem;
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orbit_data = load(filename(sett, :OrbitData), "OrbitData")
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orbit_data = decimate(orbit_data)
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info("Creating SDP problem...")
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@info("Creating SDP problem...")
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info(Base.repr(SDP_problem))
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SDP_problem, varλ, varP = SOS_problem(Δ^2, Δ, orbit_data, upper_bound=sett.upper_bound)
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JuMP.setsolver(SDP_problem, sett.solver)
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@info(Base.repr(SDP_problem))
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ws = warmstart(sett)
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@time status, (λ, Ps, ws) = PropertyT.solve(solverlog, SDP_problem, varλ, varP, ws)
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@show status
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@info("Solver's status: $status")
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save(filename(sett, :warmstart), "warmstart", ws, "Ps", Ps, "λ", λ)
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info("Reconstructing P...")
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@time P = reconstruct(Ps, orbit_data)
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@ -146,7 +149,6 @@ function distance_to_positive_cone(Δ::GroupRingElem, λ, Q; R::Int=2)
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@info("Floating point distance (to positive cone) ≈")
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@info("$(@sprintf("%.10f", distance))")
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@info("")
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if distance ≤ 0
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return distance
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@ -154,15 +156,14 @@ function distance_to_positive_cone(Δ::GroupRingElem, λ, Q; R::Int=2)
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@info("------------------------------------------------------------")
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@info("Checking in interval arithmetic...")
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@info("λ ∈ $λ")
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λ = @interval(λ)
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@info("λ ∈ $λ")
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eoi = Δ^2 - λ*Δ
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@info("Projecting columns of Q to the augmentation ideal...")
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@time Q, check = augIdproj(Interval, Q)
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@info("Checking that sum of every column contains 0.0... ")
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@info((check? "They do." : "FAILED!"))
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@info((check ? "They do." : "FAILED!"))
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check || @warn("The following numbers are meaningless!")
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@time residual = eoi - compute_SOS(parent(eoi), Q)
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@ -174,7 +175,7 @@ function distance_to_positive_cone(Δ::GroupRingElem, λ, Q; R::Int=2)
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@info("Interval distance (to positive cone) ∈")
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@info("$(distance)")
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@info("")
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@info("------------------------------------------------------------")
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return distance.lo
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end
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@ -192,11 +193,11 @@ function interpret_results(sett::Settings, sgap::Number)
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if sgap > 0
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Kazhdan_κ = Kazhdan(sgap, length(sett.S))
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if Kazhdan_κ > 0
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info("κ($(sett.name), S) ≥ $Kazhdan_κ: Group HAS property (T)!")
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@info("κ($(sett.name), S) ≥ $Kazhdan_κ: Group HAS property (T)!")
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return true
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end
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end
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info("λ($(sett.name), S) ≥ $sgap < 0: Tells us nothing about property (T)")
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@info("λ($(sett.name), S) ≥ $sgap < 0: Tells us nothing about property (T)")
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return false
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end
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@ -222,20 +223,20 @@ function check_property_T(sett::Settings)
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save(filename(sett, :solution), "λ", λ, "P", P)
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if λ < 0
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warn("Solver did not produce a valid solution!")
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@warn("Solver did not produce a valid solution!")
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end
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end
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info("λ = $λ")
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info("sum(P) = $(sum(P))")
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info("maximum(P) = $(maximum(P))")
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info("minimum(P) = $(minimum(P))")
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@info("λ = $λ")
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@info("sum(P) = $(sum(P))")
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@info("maximum(P) = $(maximum(P))")
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@info("minimum(P) = $(minimum(P))")
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isapprox(eigvals(P), abs.(eigvals(P))) ||
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@warn("The solution matrix doesn't seem to be positive definite!")
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@time Q = real(sqrtm((P+P')/2))
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sgap = distance_to_positive_cone(Δ, λ, Q, wlen=2*sett.radius)
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@time Q = real(sqrt( (P.+ P')./2 ))
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sgap = distance_to_positive_cone(Δ, λ, Q, R=2*sett.radius)
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return interpret_results(sett, sgap)
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end
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@ -2,6 +2,11 @@ __precompile__()
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module PropertyT
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using AbstractAlgebra
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using LinearAlgebra
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using SparseArrays
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using Markdown
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using Dates
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using Groups
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using GroupRings
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@ -17,5 +22,6 @@ include("RGprojections.jl")
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include("orbitdata.jl")
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include("sos_sdps.jl")
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include("checksolution.jl")
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include("1712.07167.jl")
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end # module Property(T)
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@ -3,6 +3,7 @@ module Projections
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using AbstractAlgebra
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using Groups
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using GroupRings
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using Markdown
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export PermCharacter, DirectProdCharacter, rankOne_projections
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@ -200,13 +201,13 @@ end
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#
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##############################################################################
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doc"""
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@doc doc"""
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products(X::Vector{GroupElem}, Y::Vector{GroupElem}, op=*)
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> Returns a vector of all possible products (or `op(x,y)`), where $x\in X$ and
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> $y\in Y$ are group elements. You may specify which operation is used when
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> forming 'products' by adding `op` (which is `*` by default).
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"""
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function products{T<:GroupElem}(X::AbstractVector{T}, Y::AbstractVector{T}, op=*)
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function products(X::AbstractVector{T}, Y::AbstractVector{T}, op=*) where {T<:GroupElem}
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result = Vector{T}()
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seen = Set{T}()
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for x in X
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@ -221,7 +222,7 @@ function products{T<:GroupElem}(X::AbstractVector{T}, Y::AbstractVector{T}, op=*
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return result
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end
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doc"""
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@doc doc"""
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generateGroup(gens::Vector{GroupElem}, r=2, Id=parent(first(gens))(), op=*)
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> Produces all elements of a group generated by elements in `gens` in ball of
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> radius `r` (word-length metric induced by `gens`).
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@ -230,7 +231,7 @@ doc"""
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> The identity element `Id` and binary operation function `op` can be supplied
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> to e.g. take advantage of additive group structure.
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"""
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function generateGroup{T<:GroupElem}(gens::Vector{T}, r=2, Id::T=parent(first(gens))(), op=*)
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function generateGroup(gens::Vector{T}, r=2, Id::T=parent(first(gens))(), op=*) where {T<:GroupElem}
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n = 0
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R = 1
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elts = gens
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@ -33,12 +33,12 @@ function Laplacian(S::Vector{E}, radius) where E<:AbstractAlgebra.GroupElem
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end
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function Laplacian(S, Id, radius)
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info("Generating metric ball of radius $(2radius)...")
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@info("Generating metric ball of radius $(2radius)...")
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@time E_R, sizes = Groups.generate_balls(S, Id, radius=2radius)
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info("Generated balls of sizes $sizes.")
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@info("Generated balls of sizes $sizes.")
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info("Creating product matrix...")
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@time pm = GroupRings.create_pm(E_R, GroupRings.reverse_dict(E_R), sizes[radius]; twisted=true)
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@info("Creating product matrix...")
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RG = GroupRing(parent(Id), E_R, pm)
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Δ = spLaplacian(RG, S)
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@ -52,7 +52,7 @@ end
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function loadGRElem(fname::String, G::Group)
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if isfile(fname)
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info("Loading precomputed Δ...")
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@info("Loading precomputed Δ...")
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coeffs, pm = load(fname, "coeffs", "pm")
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RG = GroupRing(G, pm)
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Δ = GroupRingElem(coeffs, RG)
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@ -12,25 +12,25 @@ struct OrbitData{T<:AbstractArray{Float64, 2}, GEl<:GroupElem, P<:perm}
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end
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function OrbitData(RG::GroupRing, autS::Group, verbose=true)
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verbose && info("Decomposing basis of RG into orbits of $(autS)")
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verbose && @info("Decomposing basis of RG into orbits of $(autS)")
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@time orbs = orbit_decomposition(autS, RG.basis, RG.basis_dict)
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@assert sum(length(o) for o in orbs) == length(RG.basis)
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verbose && info("The action has $(length(orbs)) orbits")
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verbose && @info("The action has $(length(orbs)) orbits")
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verbose && info("Projections in the Group Ring of AutS")
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@time autS_mps = Projections.rankOne_projections(GroupRing(autS))
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verbose && @info("Projections in the Group Ring of AutS = $autS")
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verbose && info("AutS-action matrix representatives")
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verbose && @info("AutS-action matrix representatives")
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@time preps = perm_reps(autS, RG.basis[1:size(RG.pm,1)], RG.basis_dict)
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@time mreps = matrix_reps(preps)
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verbose && info("Projection matrices Uπs")
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verbose && @info("Projection matrices Uπs")
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@time Uπs = [orthSVD(matrix_repr(p, mreps)) for p in autS_mps]
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multiplicities = size.(Uπs,2)
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verbose && info("multiplicities = $multiplicities")
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verbose && @info("multiplicities = $multiplicities")
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dimensions = [Int(p[autS()]*Int(order(autS))) for p in autS_mps]
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verbose && info("dimensions = $dimensions")
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verbose && @info("dimensions = $dimensions")
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@assert dot(multiplicities, dimensions) == size(RG.pm,1)
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return OrbitData(orbs, preps, Uπs, dimensions)
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@ -43,32 +43,32 @@ function decimate(od::OrbitData)
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#dimensions of the corresponding πs:
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dims = od.dims[nzros]
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return OrbitData(od.orbits, od.preps, full.(Us), dims);
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return OrbitData(od.orbits, od.preps, Array{Float64}.(Us), dims);
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end
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function orthSVD(M::AbstractMatrix{T}) where {T<:AbstractFloat}
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M = full(M)
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fact = svdfact(M)
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M_rank = sum(fact[:S] .> maximum(size(M))*eps(T))
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return fact[:U][:,1:M_rank]
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M = Matrix(M)
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fact = svd(M)
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M_rank = sum(fact.S .> maximum(size(M))*eps(T))
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return fact.U[:,1:M_rank]
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end
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function orbit_decomposition(G::Group, E::Vector, rdict=GroupRings.reverse_dict(E))
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elts = collect(elements(G))
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tovisit = trues(E);
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tovisit = trues(size(E));
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orbits = Vector{Vector{Int}}()
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orbit = zeros(Int, length(elts))
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for i in 1:endof(E)
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for i in eachindex(E)
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if tovisit[i]
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g = E[i]
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Threads.@threads for j in 1:length(elts)
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Threads.@threads for j in eachindex(elts)
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orbit[j] = rdict[elts[j](g)]
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end
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tovisit[orbit] = false
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tovisit[orbit] .= false
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push!(orbits, unique(orbit))
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end
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end
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@ -82,9 +82,9 @@ end
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###############################################################################
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dens(M::SparseMatrixCSC) = nnz(M)/length(M)
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dens(M::AbstractArray) = countnz(M)/length(M)
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dens(M::AbstractArray) = count(!iszero, M)/length(M)
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function sparsify!{Tv,Ti}(M::SparseMatrixCSC{Tv,Ti}, eps=eps(Tv); verbose=false)
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function sparsify!(M::SparseMatrixCSC{Tv,Ti}, eps=eps(Tv); verbose=false) where {Tv,Ti}
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densM = dens(M)
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for i in eachindex(M.nzval)
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@ -95,16 +95,16 @@ function sparsify!{Tv,Ti}(M::SparseMatrixCSC{Tv,Ti}, eps=eps(Tv); verbose=false)
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dropzeros!(M)
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if verbose
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info("Sparsified density:", rpad(densM, 20), " → ", rpad(dens(M), 20), " ($(nnz(M)) non-zeros)")
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@info("Sparsified density:", rpad(densM, 20), " → ", rpad(dens(M), 20), " ($(nnz(M)) non-zeros)")
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end
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return M
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end
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function sparsify!{T}(M::AbstractArray{T}, eps=eps(T); verbose=false)
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function sparsify!(M::AbstractArray{T}, eps=eps(T); verbose=false) where T
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densM = dens(M)
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if verbose
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info("Sparsifying $(size(M))-matrix... ")
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@info("Sparsifying $(size(M))-matrix... ")
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end
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for n in eachindex(M)
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@ -114,13 +114,15 @@ function sparsify!{T}(M::AbstractArray{T}, eps=eps(T); verbose=false)
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end
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if verbose
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info("$(rpad(densM, 20)) → $(rpad(dens(M),20))), ($(countnz(M)) non-zeros)")
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@info("$(rpad(densM, 20)) → $(rpad(dens(M),20))), ($(count(!iszero, M)) non-zeros)")
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end
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return sparse(M)
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end
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sparsify{T}(U::AbstractArray{T}, tol=eps(T); verbose=false) = sparsify!(deepcopy(U), tol, verbose=verbose)
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function sparsify(U::AbstractArray{T}, tol=eps(T); verbose=false) where T
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return sparsify!(deepcopy(U), tol, verbose=verbose)
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end
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###############################################################################
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#
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@ -129,7 +131,7 @@ sparsify{T}(U::AbstractArray{T}, tol=eps(T); verbose=false) = sparsify!(deepcopy
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###############################################################################
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function perm_repr(g::GroupElem, E::Vector, E_dict)
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p = Vector{Int}(length(E))
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p = Vector{Int}(undef, length(E))
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for (i,elt) in enumerate(E)
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p[i] = E_dict[g(elt)]
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end
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@ -139,7 +141,7 @@ end
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function perm_reps(G::Group, E::Vector, E_rdict=GroupRings.reverse_dict(E))
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elts = collect(elements(G))
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l = length(elts)
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preps = Vector{perm}(l)
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preps = Vector{perm}(undef, l)
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permG = PermutationGroup(length(E))
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@ -151,13 +153,13 @@ function perm_reps(G::Group, E::Vector, E_rdict=GroupRings.reverse_dict(E))
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end
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function matrix_repr(x::GroupRingElem, mreps::Dict)
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nzeros = findn(x.coeffs)
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nzeros = findall(!iszero, x.coeffs)
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return sum(x[i].*mreps[parent(x).basis[i]] for i in nzeros)
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end
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function matrix_reps(preps::Dict{T,perm{I}}) where {T<:GroupElem, I<:Integer}
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kk = collect(keys(preps))
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mreps = Vector{SparseMatrixCSC{Float64, Int}}(length(kk))
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mreps = Vector{SparseMatrixCSC{Float64, Int}}(undef, length(kk))
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Threads.@threads for i in 1:length(kk)
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mreps[i] = AbstractAlgebra.matrix_repr(preps[kk[i]])
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end
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@ -75,7 +75,7 @@ function SOS_problem(X::GroupRingElem, orderunit::GroupRingElem, data::OrbitData
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Ns = size.(data.Uπs, 2)
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m = JuMP.Model();
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P = Vector{Matrix{JuMP.Variable}}(length(Ns))
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P = Vector{Matrix{JuMP.Variable}}(undef, length(Ns))
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for (k,s) in enumerate(Ns)
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P[k] = JuMP.@variable(m, [i=1:s, j=1:s])
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@ -87,7 +87,7 @@ function SOS_problem(X::GroupRingElem, orderunit::GroupRingElem, data::OrbitData
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JuMP.@constraint(m, λ <= upper_bound)
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end
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info("Adding $(length(data.orbits)) constraints... ")
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@info("Adding $(length(data.orbits)) constraints... ")
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@time addconstraints!(m,P,λ,X,orderunit, data)
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|
||||
@ -96,7 +96,7 @@ function SOS_problem(X::GroupRingElem, orderunit::GroupRingElem, data::OrbitData
|
||||
end
|
||||
|
||||
function constraintLHS!(M, cnstr, Us, Ust, dims, eps=1000*eps(eltype(first(M))))
|
||||
for π in 1:endof(Us)
|
||||
for π in eachindex(Us)
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||||
M[π] = PropertyT.sparsify!(dims[π].*Ust[π]*cnstr*Us[π], eps)
|
||||
end
|
||||
end
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||||
@ -112,13 +112,13 @@ function addconstraints!(m::JuMP.Model,
|
||||
cnstrs = constraints(parent(X).pm)
|
||||
orb_cnstr = spzeros(Float64, size(parent(X).pm)...)
|
||||
|
||||
M = [Array{Float64}(n,n) for n in size.(UπsT,1)]
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||||
M = [Array{Float64}(undef, n,n) for n in size.(UπsT,1)]
|
||||
|
||||
for (t, orbit) in enumerate(data.orbits)
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||||
orbit_constraint!(orb_cnstr, cnstrs, orbit)
|
||||
constraintLHS!(M, orb_cnstr, data.Uπs, UπsT, data.dims)
|
||||
|
||||
lhs = @expression(m, sum(vecdot(M[π], P[π]) for π in 1:endof(data.Uπs)))
|
||||
lhs = @expression(m, sum(dot(M[π], P[π]) for π in eachindex(data.Uπs)))
|
||||
x, u = X_orb[t], orderunit_orb[t]
|
||||
JuMP.@constraint(m, lhs == x - λ*u)
|
||||
end
|
||||
@ -198,8 +198,8 @@ function solve(solverlog::String, model::JuMP.Model, varλ::JuMP.Variable, varP,
|
||||
|
||||
isdir(dirname(solverlog)) || mkpath(dirname(solverlog))
|
||||
|
||||
Base.flush(Base.stdout)
|
||||
status, (λ, P, warmstart) = open(solverlog, "a+") do logfile
|
||||
Base.Libc.flush_cstdio()
|
||||
redirect_stdout(logfile) do
|
||||
status, (λ, P, warmstart) = PropertyT.solve(model, varλ, varP, warmstart)
|
||||
Base.Libc.flush_cstdio()
|
||||
@ -224,7 +224,7 @@ function fillfrominternal!(m::JuMP.Model, traits)
|
||||
m.objBound = NaN
|
||||
m.objVal = NaN
|
||||
m.colVal = fill(NaN, numCols)
|
||||
m.linconstrDuals = Array{Float64}(0)
|
||||
m.linconstrDuals = Array{Float64}(undef, 0)
|
||||
|
||||
discrete = (traits.int || traits.sos)
|
||||
|
||||
@ -262,7 +262,7 @@ function fillfrominternal!(m::JuMP.Model, traits)
|
||||
@assert length(infray) == numRows
|
||||
infray
|
||||
catch
|
||||
suppress_warnings || warn("Infeasibility ray (Farkas proof) not available")
|
||||
@warn("Infeasibility ray (Farkas proof) not available")
|
||||
fill(NaN, numRows)
|
||||
end
|
||||
elseif stat == :Unbounded
|
||||
@ -271,7 +271,7 @@ function fillfrominternal!(m::JuMP.Model, traits)
|
||||
@assert length(unbdray) == numCols
|
||||
unbdray
|
||||
catch
|
||||
suppress_warnings || warn("Unbounded ray not available")
|
||||
@warn("Unbounded ray not available")
|
||||
fill(NaN, numCols)
|
||||
end
|
||||
end
|
||||
@ -292,7 +292,10 @@ function fillfrominternal!(m::JuMP.Model, traits)
|
||||
# Do a separate try since getobjval could work while getobjbound does not and vice versa
|
||||
objBound = MathProgBase.getobjbound(m.internalModel) + m.obj.aff.constant
|
||||
m.objBound = objBound
|
||||
catch
|
||||
@warn("objBound could not be obtained")
|
||||
end
|
||||
|
||||
try
|
||||
objVal = MathProgBase.getobjval(m.internalModel) + m.obj.aff.constant
|
||||
colVal = MathProgBase.getsolution(m.internalModel)[1:numCols]
|
||||
@ -304,8 +307,15 @@ function fillfrominternal!(m::JuMP.Model, traits)
|
||||
# Don't corrupt the answers if one of the above two calls fails
|
||||
m.objVal = objVal
|
||||
m.colVal = colVal
|
||||
catch
|
||||
@warn("objVal/colVal could not be obtained")
|
||||
end
|
||||
end
|
||||
|
||||
if traits.conic && m.objSense == :Max
|
||||
m.objBound = -1 * (m.objBound - m.obj.aff.constant) + m.obj.aff.constant
|
||||
m.objVal = -1 * (m.objVal - m.obj.aff.constant) + m.obj.aff.constant
|
||||
end
|
||||
|
||||
return stat
|
||||
end
|
||||
|
||||
Loading…
Reference in New Issue
Block a user