Merge branch 'enh/rework-logging' of https://git.wmi.amu.edu.pl/kalmar/PropertyT.jl into enh/rework-logging

src/CheckSolution.jl

@@ -36,9 +36,8 @@ function compute_SOS(Q::AbstractArray, pm::Array{Int,2}, l::Int)
         groupring_square(Q[:,i], l, pm)
     end
 
-   println("")
-
     return result
+
 end
 
 function compute_SOS(Q::AbstractArray, RG::GroupRing, l::Int)
@@ -46,32 +45,13 @@ function compute_SOS(Q::AbstractArray, RG::GroupRing, l::Int)
     return GroupRingElem(result, RG)
 end
 
-function distance_to_cone{T<:Interval}(elt::GroupRingElem, Q::AbstractArray{T,2}, wlen::Int)
+function distances_to_cone(elt::GroupRingElem, wlen::Int)
-   SOS = compute_SOS(Q, parent(elt), length(elt.coeffs))
+    ɛ_dist = GroupRings.augmentation(elt)
-   SOS_diff = elt - SOS
 
-   ɛ_dist = GroupRings.augmentation(SOS_diff)
+    eoi_SOS_L1_dist = norm(elt,1)
-   info(logger, "ɛ(∑ξᵢ*ξᵢ) ∈ $(ɛ_dist)")
-
-   eoi_SOS_L1_dist = norm(SOS_diff,1)
-   info(logger, "‖Δ² - λΔ - ∑ξᵢ*ξᵢ‖₁ ∈ $(eoi_SOS_L1_dist)")
 
     dist = 2^(wlen-1)*eoi_SOS_L1_dist
-   return dist
+    return dist, ɛ_dist, eoi_SOS_L1_dist
-end
-
-function distance_to_cone{T}(elt::GroupRingElem, Q::AbstractArray{T,2}, wlen::Int)
-   SOS = compute_SOS(Q, parent(elt), length(elt.coeffs))
-   SOS_diff = elt - SOS
-
-   ɛ_dist = GroupRings.augmentation(SOS_diff)
-   info(logger, "ɛ(Δ² - λΔ - ∑ξᵢ*ξᵢ) ≈ $(@sprintf("%.10f", ɛ_dist))")
-
-   eoi_SOS_L1_dist = norm(SOS_diff,1)
-   info(logger, "‖Δ² - λΔ - ∑ξᵢ*ξᵢ‖₁ ≈ $(@sprintf("%.10f", eoi_SOS_L1_dist))")
-
-   dist = 2^(wlen-1)*eoi_SOS_L1_dist
-   return dist
 end
 
 function augIdproj{T, I<:AbstractInterval}(S::Type{I}, Q::AbstractArray{T,2})
@@ -99,30 +79,56 @@ function augIdproj{T}(Q::AbstractArray{T,2}, logger)
     return Q
 end
 
-function distance_to_positive_cone(Δ::GroupRingElem, λ, Q, wlen::Int)
+function distance_to_cone(elt::GroupRingElem, λ::T, Q::AbstractArray{T,2}, wlen::Int, logger) where {T<:AbstractFloat}
+
     info(logger, "------------------------------------------------------------")
     info(logger, "λ = $λ")
     info(logger, "Checking in floating-point arithmetic...")
-    Δ²_λΔ = EOI(Δ, λ)
+    @logtime logger SOS_diff = elt - compute_SOS(Q, parent(elt), length(elt.coeffs))
-    @logtime logger fp_distance = λ - distance_to_cone(Δ²_λΔ, Q, wlen)
+    dist, ɛ_dist, eoi_SOS_L1_dist = distances_to_cone(SOS_diff, wlen)
-    info(logger, "Floating point distance (to positive cone) ≈ $(@sprintf("%.10f", fp_distance))")
+    info(logger, "ɛ(Δ² - λΔ - ∑ξᵢ*ξᵢ) ≈ $(@sprintf("%.10f", ɛ_dist))")
+    info(logger, "‖Δ² - λΔ - ∑ξᵢ*ξᵢ‖₁ ≈ $(@sprintf("%.10f", eoi_SOS_L1_dist))")
+
+    fp_distance = λ - dist
+
+    info(logger, "Floating point distance (to positive cone) ≈")
+    info(logger, "$(@sprintf("%.10f", fp_distance))")
+    info(logger, "")
+
+    return fp_distance
+end
+
+function distance_to_cone(elt::GroupRingElem, λ::T, Q::AbstractArray{T,2}, wlen::Int, logger) where {T<:AbstractInterval}
     info(logger, "------------------------------------------------------------")
+    info(logger, "λ = $λ")
+    info(logger, "Checking in interval arithmetic...")
+    @logtime logger SOS_diff = elt - compute_SOS(Q, parent(elt), length(elt.coeffs))
+    dist, ɛ_dist, eoi_SOS_L1_dist = distances_to_cone(SOS_diff, wlen)
+    info(logger, "ɛ(∑ξᵢ*ξᵢ) ∈ $(ɛ_dist)")
+    info(logger, "‖Δ² - λΔ - ∑ξᵢ*ξᵢ‖₁ ∈ $(eoi_SOS_L1_dist)")
+
+    int_distance = λ - dist
+
+    info(logger, "The Augmentation-projected actual distance (to positive cone) ∈")
+    info(logger, "$(int_distance)")
+    info(logger, "")
+
+    return int_distance
+end
+
+function check_distance_to_cone(Δ::GroupRingElem, λ, Q, wlen::Int, logger)
+
+    fp_distance = distance_to_cone(EOI(Δ, λ), λ, Q, wlen, logger)
 
     if fp_distance ≤ 0
         return fp_distance
     end
 
-    info(logger, "")
-    Q = augIdproj(Q, logger)
-
-    info(logger, "Checking in interval arithmetic")
     λ = @interval(λ)
     Δ = GroupRingElem([@interval(c) for c in Δ.coeffs], parent(Δ))
-    Δ²_λΔ = EOI(Δ, λ)
+    Q = augIdproj(Q, logger)
 
-    @logtime logger Interval_dist_to_ΣSq = λ - distance_to_cone(Δ²_λΔ, Q, wlen)
+    int_distance = distance_to_cone(EOI(Δ, λ), λ, Q, wlen, logger)
-    info(logger, "The Augmentation-projected actual distance (to positive cone) ∈ $(Interval_dist_to_ΣSq)")
-    info(logger, "------------------------------------------------------------")
 
-    return Interval_dist_to_ΣSq
+    return int_distance.lo
 end

src/Orbit-wise.jl

@@ -14,6 +14,7 @@ immutable Settings{T<:AbstractMathProgSolver}
     upper_bound::Float64
     tol::Float64
     warmstart::Bool
+    logger
 end
 
 prefix(s::Settings) = s.name
@@ -32,23 +33,23 @@ immutable OrbitData{T<:AbstractArray{Float64, 2}, LapType <:AbstractVector{Float
 end
 
 function OrbitData(sett::Settings)
-   splap = load(joinpath(prepath(sett), "delta.jld"), "Δ");
+    splap = load(filename(prepath(sett), :Δ), "Δ");
-   pm = load(joinpath(prepath(sett), "pm.jld"), "pm");
+    pm = load(filename(prepath(sett), :pm), "pm");
     cnstr = PropertyT.constraints(pm);
     splap² = similar(splap)
     splap² = GroupRings.mul!(splap², splap, splap, pm);
 
-   Uπs = load(joinpath(prepath(sett), "U_pis.jld"), "Uπs")
+    Uπs = load(filename(prepath(sett), :Uπs), "Uπs")
     nzros = [i for i in 1:length(Uπs) if size(Uπs[i],2) !=0]
     Uπs = Uπs[nzros]
     Uπs = sparsify!.(Uπs, sett.tol, check=true, verbose=true)
 
     #dimensions of the corresponding πs:
-   dims = load(joinpath(prepath(sett), "U_pis.jld"), "dims")[nzros]
+    dims = load(filename(prepath(sett), :Uπs), "dims")[nzros]
 
     m, P = init_model(size(Uπs,1), [size(U,2) for U in Uπs]);
 
-   orbits = load(joinpath(prepath(sett), "orbits.jld"), "orbits");
+    orbits = load(filename(prepath(sett), :orb), "orbits");
     n = size(Uπs[1],1)
     orb_spcnstrm = [orbit_constraint(cnstr[collect(orb)], n) for orb in orbits]
     orb_splap = orbit_spvector(splap, orbits)
@@ -79,7 +80,7 @@ function sparsify!{Tv,Ti}(M::SparseMatrixCSC{Tv,Ti}, eps=eps(Tv); verbose=false)
     m = nnz(M)
 
     if verbose
-      info(logger, "Sparsified density:", rpad(densM, 20), " → ", rpad(dens(M), 20))
+        info("Sparsified density:", rpad(densM, 20), " → ", rpad(dens(M), 20))
     end
 
     return M
@@ -91,11 +92,11 @@ function sparsify!{T}(M::AbstractArray{T}, eps=eps(T); check=false, verbose=fals
     M[abs.(M) .< eps] .= zero(T)
 
     if check && rankM != rank(M)
-      warn(logger, "Sparsification decreased the rank!")
+        warn("Sparsification decreased the rank!")
     end
 
     if verbose
-      info(logger, "Sparsified density:", rpad(densM, 20), " → ", rpad(dens(M),20))
+        info("Sparsified density:", rpad(densM, 20), " → ", rpad(dens(M),20))
     end
 
     return sparse(M)
@@ -103,19 +104,6 @@ end
 
 sparsify{T}(U::AbstractArray{T}, tol=eps(T); check=true, verbose=false) = sparsify!(deepcopy(U), tol, check=check, verbose=verbose)
 
-function init_orbit_data(logger, sett::Settings; radius=2)
-
-   ex(fname) = isfile(joinpath(prepath(sett), fname))
-
-   files_exists = ex.(["delta.jld", "pm.jld", "U_pis.jld", "orbits.jld", "preps.jld"])
-
-   if !all(files_exists)
-      compute_orbit_data(logger, prepath(sett), 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)
@@ -178,8 +166,8 @@ function init_model(n, sizes)
 end
 
 function create_SDP_problem(sett::Settings)
-   info(logger, "Loading orbit data....")
+    info(sett.logger, "Loading orbit data....")
-   @logtime logger SDP_problem, orb_data = OrbitData(sett);
+    @logtime sett.logger SDP_problem, orb_data = OrbitData(sett);
 
     if sett.upper_bound < Inf
         λ = JuMP.getvariable(SDP_problem, :λ)
@@ -187,8 +175,8 @@ function create_SDP_problem(sett::Settings)
     end
 
     t = length(orb_data.laplacian)
-   info(logger, "Adding $t constraints ... ")
+    info(sett.logger, "Adding $t constraints ... ")
-   @logtime logger addconstraints!(SDP_problem, orb_data)
+    @logtime sett.logger addconstraints!(SDP_problem, orb_data)
 
     return SDP_problem, orb_data
 end
@@ -201,16 +189,16 @@ function λandP(m::JuMP.Model, data::OrbitData, warmstart=true)
 end
 
 function λandP(m::JuMP.Model, data::OrbitData, sett::Settings)
-   info(logger, "Solving SDP problem...")
+    info(sett.logger, "Solving SDP problem...")
-   λ, Ps = λandP(m, data, sett.warmstart)
+    @logtime sett.logger λ, Ps = λandP(m, data, sett.warmstart)
 
-   info(logger, "Reconstructing P...")
+    info(sett.logger, "Reconstructing P...")
 
-   preps = load_preps(joinpath(prepath(sett), "preps.jld"), sett.autS)
+    preps = load_preps(filename(prepath(sett), :preps), sett.autS)
 
-   @logtime logger recP = reconstruct_sol(preps, data.Us, Ps, data.dims)
+    @logtime sett.logger recP = reconstruct_sol(preps, data.Us, Ps, data.dims)
 
-   fname = PropertyT.λSDPfilenames(fullpath(sett))[2]
+    fname = filename(fullpath(sett), :P)
     save(fname, "origP", Ps, "P", recP)
     return λ, recP
 end
@@ -229,49 +217,39 @@ end
 
 function check_property_T(sett::Settings)
 
-   init_orbit_data(logger, sett, radius=sett.radius)
+    ex(s) = exists(filename(prepath(sett), s))
 
-   if !sett.warmstart && all(isfile.(λSDPfilenames(fullpath(sett))))
+    files_exists = ex.([:pm, :Δ, :Uπs, :orb, :preps])
+
+    if !all(files_exists)
+        compute_orbit_data(sett.logger, prepath(sett), sett.S, sett.autS, radius=sett.radius)
+    end
+
+    cond1 = exists(filename(fullpath(sett), :λ))
+    cond2 = exists(filename(fullpath(sett), :P))
+
+    if !sett.warmstart && cond1 && cond2
         λ, P = PropertyT.λandP(fullpath(sett))
     else
-      info(logger, "Creating SDP problem...")
+        info(sett.logger, "Creating SDP problem...")
         SDP_problem, orb_data = create_SDP_problem(sett)
         JuMP.setsolver(SDP_problem, sett.solver)
+        info(sett.logger, Base.repr(SDP_problem))
 
         λ, P = λandP(SDP_problem, orb_data, sett)
     end
 
-   info(logger, "λ = $λ")
+    info(sett.logger, "λ = $λ")
-   info(logger, "sum(P) = $(sum(P))")
+    info(sett.logger, "sum(P) = $(sum(P))")
-   info(logger, "maximum(P) = $(maximum(P))")
+    info(sett.logger, "maximum(P) = $(maximum(P))")
-   info(logger, "minimum(P) = $(minimum(P))")
+    info(sett.logger, "minimum(P) = $(minimum(P))")
-
-   if λ > 0
-      pm_fname, Δ_fname = pmΔfilenames(prepath(sett))
-      RG = GroupRing(sett.G, load(pm_fname, "pm"))
-      Δ = GroupRingElem(load(Δ_fname, "Δ")[:, 1], RG)
 
     isapprox(eigvals(P), abs.(eigvals(P)), atol=sett.tol) ||
         warn("The solution matrix doesn't seem to be positive definite!")
-     #  @assert P == Symmetric(P)
-      @logtime logger Q = real(sqrtm(Symmetric(P)))
 
-      sgap = distance_to_positive_cone(Δ, λ, Q, 2*sett.radius)
+    if λ > 0
-      if isa(sgap, Interval)
+        return check_λ(sett.name, sett.S, λ, P, sett.radius, sett.logger)
-           sgap = sgap.lo
     end
-      if sgap > 0
+    info(sett.logger, "κ($(sett.name), S) ≥ $λ < 0: Tells us nothing about property (T)")
-           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) ≥ $λ < 0: Tells us nothing about property (T)")
     return false
 end

src/OrbitDecomposition.jl

@@ -166,12 +166,13 @@ function orthSVD{T}(M::AbstractMatrix{T})
     return fact[:U][:,1:M_rank]
 end
 
-function compute_orbit_data{T<:GroupElem}(logger, name::String, G::Nemo.Group, S::Vector{T}, autS::Nemo.Group; radius=2)
+function compute_orbit_data{T<:GroupElem}(logger, name::String, S::Vector{T}, autS::Nemo.Group; radius=2)
     isdir(name) || mkdir(name)
 
     info(logger, "Generating ball of radius $(2*radius)")
 
     # TODO: Fix that by multiple dispatch?
+    G = parent(first(S))
     Id = (isa(G, Nemo.Ring) ? one(G) : G())
 
     @logtime logger E_2R, sizes = Groups.generate_balls(S, Id, radius=2*radius);
@@ -182,10 +183,10 @@ function compute_orbit_data{T<:GroupElem}(logger, name::String, G::Nemo.Group, S
     info(logger, "Product matrix")
     @logtime logger pm = GroupRings.create_pm(E_2R, E_rdict, sizes[radius], twisted=true)
     RG = GroupRing(G, E_2R, E_rdict, pm)
-   Δ = PropertyT.splaplacian(RG, S)
+    Δ = PropertyT.spLaplacian(RG, S)
     @assert GroupRings.augmentation(Δ) == 0
-   save(joinpath(name, "delta.jld"), "Δ", Δ.coeffs)
+    save(filename(name, :Δ), "Δ", Δ.coeffs)
-   save(joinpath(name, "pm.jld"), "pm", pm)
+    save(filename(name, :pm), "pm", pm)
 
     info(logger, "Decomposing E into orbits of $(autS)")
     @logtime logger orbs = orbit_decomposition(autS, E_2R, E_rdict)
@@ -195,7 +196,7 @@ function compute_orbit_data{T<:GroupElem}(logger, name::String, G::Nemo.Group, S
 
     info(logger, "Action matrices")
     @logtime logger reps = perm_reps(autS, E_2R[1:sizes[radius]], E_rdict)
-   save_preps(joinpath(name, "preps.jld"), reps)
+    save_preps(filename(name, :preps), reps)
     reps = matrix_reps(reps)
 
     info(logger, "Projections")

src/PropertyT.jl

@@ -12,18 +12,28 @@ using MathProgBase
 
 using Memento
 
-const logger = Memento.config("info", fmt="{msg}")
-const solver_logger = Memento.config("info", fmt="{msg}")
-
 function setup_logging(name::String)
     isdir(name) || mkdir(name)
+    L = Memento.config("info", fmt="{date}| {msg}")
 
-   Memento.add_handler(logger,
+    handler = Memento.DefaultHandler(
-      Memento.DefaultHandler(joinpath(name,"full_$(string((now()))).log"),
+        filename(name, :logall), Memento.DefaultFormatter("{date}| {msg}"))
-      Memento.DefaultFormatter("{date}| {msg}")), "full_log")
 
-   e = redirect_stderr(logger.handlers["full_log"].io)
+    handler.levels.x = L.levels
+    L.handlers["all"] = handler
 
+    # e = redirect_stderr(L.handlers["all"].io)
+
+    return L
+end
+
+function solverlogger(name)
+    logger = Memento.config("info", fmt="{msg}")
+
+    handler = DefaultHandler(
+        filename(name, :logsolver), DefaultFormatter("{date}| {msg}"))
+    handler.levels.x = logger.levels
+    logger.handlers["solver_log"] = handler
     return logger
 end
 
@@ -36,7 +46,7 @@ macro logtime(logger, ex)
         local diff = Base.GC_Diff(Base.gc_num(), stats)
         local ts = time_string(elapsedtime, diff.allocd, diff.total_time,
                                Base.gc_alloc_count(diff))
-        esc(info(logger, ts))
+        $(esc(info))($(esc(logger)), ts)
         val
     end
 end
@@ -66,277 +76,112 @@ function time_string(elapsedtime, bytes, gctime, allocs)
     return str
 end
 
-function exists(fname::String)
+exists(fname::String) = isfile(fname) || islink(fname)
-   return isfile(fname) || islink(fname)
+
+filename(prefix, s::Symbol) = filename(prefix, Val{s})
+
+@eval begin
+    for (s,n) in [
+        [:pm,   "pm.jld"],
+        [:Δ,    "delta.jld"],
+        [:λ,    "lambda.jld"],
+        [:P,    "SDPmatrix.jld"],
+        [:warm, "warmstart.jld"],
+        [:Uπs,  "U_pis.jld"],
+        [:orb,  "orbits.jld"],
+        [:preps,"preps.jld"],
+
+        [:logall,   "full_$(string(now())).log"],
+        [:logsolver,"solver_$(string(now())).log"]
+        ]
+
+        filename(prefix::String, ::Type{Val{$:(s)}}) = joinpath(prefix, :($n))
+    end
 end
 
-function pmΔfilenames(prefix::String)
+function Laplacian(name::String, G::Group)
-   isdir(prefix) || mkdir(prefix)
+    if exists(filename(name, :Δ)) && exists(filename(name, :pm))
-   pm_filename = joinpath(prefix, "pm.jld")
+        RG = GroupRing(G, load(filename(name, :pm), "pm"))
-   Δ_coeff_filename = joinpath(prefix, "delta.jld")
+        Δ = GroupRingElem(load(filename(name, :Δ), "Δ")[:, 1], RG)
-   return pm_filename, Δ_coeff_filename
+    else
+        throw("You need to precompute $(filename(name, :pm)) and $(filename(name, :Δ)) to load it!")
+    end
+    return Δ
 end
 
-function λSDPfilenames(prefix::String)
+function Laplacian{T<:GroupElem}(S::Vector{T}, Id::T,
-   isdir(prefix) || mkdir(prefix)
+    logger=getlogger(); radius::Int=2)
-   λ_filename = joinpath(prefix, "lambda.jld")
-   SDP_filename = joinpath(prefix, "SDPmatrix.jld")
-   return λ_filename, SDP_filename
-end
 
-function ΔandSDPconstraints(prefix::String, G::Group)
+    info(logger, "Generating metric ball of radius $radius...")
-    info(logger, "Loading precomputed pm, Δ, sdp_constraints...")
-    pm_fname, Δ_fname = pmΔfilenames(prefix)
-
-    product_matrix = load(pm_fname, "pm")
-    sdp_constraints = constraints(product_matrix)
-
-    RG = GroupRing(G, product_matrix)
-    Δ = GroupRingElem(load(Δ_fname, "Δ")[:, 1], RG)
-
-    return Δ, sdp_constraints
-end
-
-function ΔandSDPconstraints{T<:GroupElem}(name::String, S::Vector{T}, Id::T; radius::Int=2)
-   info(logger, "Computing pm, Δ, sdp_constraints...")
-   Δ, sdp_constraints = ΔandSDPconstraints(S, Id, radius=radius)
-   pm_fname, Δ_fname = pmΔfilenames(name)
-   save(pm_fname, "pm", parent(Δ).pm)
-   save(Δ_fname, "Δ", Δ.coeffs)
-   return Δ, sdp_constraints
-end
-
-function ΔandSDPconstraints{T<:GroupElem}(S::Vector{T}, Id::T; radius::Int=2)
-    info(logger, "Generating balls of sizes $sizes")
     @logtime logger E_R, sizes = Groups.generate_balls(S, Id, radius=2*radius)
+    info(logger, "Generated balls of sizes $sizes.")
 
     info(logger, "Creating product matrix...")
     @logtime logger pm = GroupRings.create_pm(E_R, GroupRings.reverse_dict(E_R), sizes[radius]; twisted=true)
 
-    info(logger, "Creating sdp_constratints...")
-    @logtime logger sdp_constraints = PropertyT.constraints(pm)
-
     RG = GroupRing(parent(Id), E_R, pm)
 
-    Δ = splaplacian(RG, S)
+    Δ = spLaplacian(RG, S)
-    return Δ, sdp_constraints
+    return Δ
 end
 
 function λandP(name::String)
-    λ_fname, SDP_fname = λSDPfilenames(name)
+    λ_fname = filename(name, :λ)
-    f₁ = exists(λ_fname)
+    P_fname = filename(name, :P)
-    f₂ = exists(SDP_fname)
 
-    if f₁ && f₂
+    if exists(λ_fname) && exists(P_fname)
-        info(logger, "Loading precomputed λ, P...")
         λ = load(λ_fname, "λ")
-        P = load(SDP_fname, "P")
+        P = load(P_fname, "P")
     else
-        throw(ArgumentError("You need to precompute λ and SDP matrix P to load it!"))
+        throw("You need to precompute $λ_fname and $P_fname to load it!")
     end
     return λ, P
 end
 
-function λandP(name::String, SDP_problem::JuMP.Model, varλ, varP, warmstart=false)
+function λandP(name::String, SDP::JuMP.Model, varλ, varP, warmstart=true)
-   add_handler(solver_logger,
+
-      DefaultHandler(joinpath(name, "solver_$(string(now())).log"),
+    if warmstart && isfile(filename(name, :warm))
-      DefaultFormatter("{date}| {msg}")),
+        ws = load(filename(name, :warm), "warmstart")
-      "solver_log")
-   if warmstart && isfile(joinpath(name, "warmstart.jld"))
-      ws = load(joinpath(name, "warmstart.jld"), "warmstart")
     else
         ws = nothing
     end
 
-   λ, P, warmstart = compute_λandP(SDP_problem, varλ, varP, warmstart=ws)
+    solver_log = solverlogger(name)
 
-   remove_handler(solver_logger, "solver_log")
+    Base.Libc.flush_cstdio()
+    o = redirect_stdout(solver_log.handlers["solver_log"].io)
+    Base.Libc.flush_cstdio()
 
-   λ_fname, P_fname = λSDPfilenames(name)
+    λ, P, warmstart = solve_SDP(SDP, varλ, varP, warmstart=ws)
+
+    Base.Libc.flush_cstdio()
+    redirect_stdout(o)
+
+    delete!(solver_log.handlers, "solver_log")
 
     if λ > 0
-       save(λ_fname, "λ", λ)
+        save(filename(name, :λ), "λ", λ)
-       save(P_fname, "P", P)
+        save(filename(name, :P), "P", P)
-       save(joinpath(name, "warmstart.jld"), "warmstart", warmstart)
+        save(filename(name, :warm), "warmstart", warmstart)
     else
-       throw(ErrorException("Solver did not produce a valid solution!: λ = $λ"))
+        throw(ErrorException("Solver did not produce a valid solution: λ = $λ"))
     end
     return λ, P
-
-end
-
-function fillfrominternal!(m::JuMP.Model, traits)
-    # Copied from JuMP/src/solvers.jl:178
-
-    stat::Symbol = MathProgBase.status(m.internalModel)
-
-    numRows, numCols = length(m.linconstr), m.numCols
-    m.objBound = NaN
-    m.objVal = NaN
-    m.colVal = fill(NaN, numCols)
-    m.linconstrDuals = Array{Float64}(0)
-
-    discrete = (traits.int || traits.sos)
-
-    if stat == :Optimal
-        # If we think dual information might be available, try to get it
-        # If not, return an array of the correct length
-        if discrete
-            m.redCosts = fill(NaN, numCols)
-            m.linconstrDuals = fill(NaN, numRows)
-        else
-            if !traits.conic
-                m.redCosts = try
-                    MathProgBase.getreducedcosts(m.internalModel)[1:numCols]
-                catch
-                    fill(NaN, numCols)
-                end
-
-                m.linconstrDuals = try
-                    MathProgBase.getconstrduals(m.internalModel)[1:numRows]
-                catch
-                    fill(NaN, numRows)
-                end
-            elseif !traits.qp && !traits.qc
-                JuMP.fillConicDuals(m)
-            end
-        end
-    else
-        # Problem was not solved to optimality, attempt to extract useful
-        # information anyway
-
-        if traits.lin
-            if stat == :Infeasible
-                m.linconstrDuals = try
-                    infray = MathProgBase.getinfeasibilityray(m.internalModel)
-                    @assert length(infray) == numRows
-                    infray
-                catch
-                    suppress_warnings || warn("Infeasibility ray (Farkas proof) not available")
-                    fill(NaN, numRows)
-                end
-            elseif stat == :Unbounded
-                m.colVal = try
-                    unbdray = MathProgBase.getunboundedray(m.internalModel)
-                    @assert length(unbdray) == numCols
-                    unbdray
-                catch
-                    suppress_warnings || warn("Unbounded ray not available")
-                    fill(NaN, numCols)
-                end
-            end
-        end
-        # conic duals (currently, SOC and SDP only)
-        if !discrete && traits.conic && !traits.qp && !traits.qc
-            if stat == :Infeasible
-                JuMP.fillConicDuals(m)
-            end
-        end
-    end
-
-    # If the problem was solved, or if it terminated prematurely, try
-    # to extract a solution anyway. This commonly occurs when a time
-    # limit or tolerance is set (:UserLimit)
-    if !(stat == :Infeasible || stat == :Unbounded)
-        try
-            # 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
-        end
-        try
-            objVal = MathProgBase.getobjval(m.internalModel) + m.obj.aff.constant
-            colVal = MathProgBase.getsolution(m.internalModel)[1:numCols]
-            # Rescale off-diagonal terms of SDP variables
-            if traits.sdp
-                offdiagvars = JuMP.offdiagsdpvars(m)
-                colVal[offdiagvars] /= sqrt(2)
-            end
-            # Don't corrupt the answers if one of the above two calls fails
-            m.objVal = objVal
-            m.colVal = colVal
-        end
-    end
-
-    return stat
-end
-
-function compute_λandP(m, varλ, varP; warmstart=nothing)
-    λ = 0.0
-    P = nothing
-
-    traits = JuMP.ProblemTraits(m, relaxation=false)
-
-    while λ == 0.0
-        try
-            JuMP.build(m, traits=traits)
-            if warmstart != nothing
-                p_sol, d_sol, s = warmstart
-                MathProgBase.SolverInterface.setwarmstart!(m.internalModel, p_sol; dual_sol = d_sol, slack=s);
-            end
-            solve_SDP(m)
-            λ = MathProgBase.getobjval(m.internalModel)
-        catch y
-            warn(solver_logger, y)
-        end
-    end
-
-    warmstart = (m.internalModel.primal_sol, m.internalModel.dual_sol,
-          m.internalModel.slack)
-
-    fillfrominternal!(m, traits)
-
-    P = JuMP.getvalue(varP)
-    λ = JuMP.getvalue(varλ)
-
-    return λ, P, warmstart
 end
 
 Kazhdan_from_sgap(λ,N) = sqrt(2*λ/N)
 
-function check_property_T(name::String, S, Id, solver, upper_bound, tol, radius)
+function check_λ(name, S, λ, P, radius, logger)
 
-    isdir(name) || mkdir(name)
+    RG = GroupRing(parent(first(S)), load(filename(name, :pm), "pm"))
+    Δ = GroupRingElem(load(filename(name, :Δ), "Δ")[:, 1], RG)
 
-    if all(exists.(pmΔfilenames(name)))
-        # cached
-        Δ, sdp_constraints = ΔandSDPconstraints(name, parent(S[1]))
-    else
-        # compute
-        Δ, sdp_constraints = ΔandSDPconstraints(name, S, Id, radius=radius)
-    end
-
-   if all(exists.(λSDPfilenames(name)))
-      λ, P = λandP(name)
-   else
-      info(logger, "Creating SDP problem...")
-      SDP_problem, λ, P = create_SDP_problem(Δ, sdp_constraints, upper_bound=upper_bound)
-      JuMP.setsolver(SDP_problem, solver)
-
-
-      λ, P = λandP(name, SDP_problem, λ, P)
-   end
-
-   info(logger, "λ = $λ")
-   info(logger, "sum(P) = $(sum(P))")
-   info(logger, "maximum(P) = $(maximum(P))")
-   info(logger, "minimum(P) = $(minimum(P))")
-
-   if λ > 0
-      pm_fname, Δ_fname = pmΔfilenames(name)
-      RG = GroupRing(parent(first(S)), load(pm_fname, "pm"))
-      Δ = GroupRingElem(load(Δ_fname, "Δ")[:, 1], RG)
-
-      isapprox(eigvals(P), abs(eigvals(P)), atol=tol) ||
-         warn("The solution matrix doesn't seem to be positive definite!")
-     #  @assert P == Symmetric(P)
     @logtime logger Q = real(sqrtm(Symmetric(P)))
 
-      sgap = distance_to_positive_cone(Δ, λ, Q, 2*radius)
+    sgap = check_distance_to_cone(Δ, λ, Q, 2*radius, logger)
-      if isa(sgap, Interval)
+
-         sgap = sgap.lo
-      end
     if sgap > 0
-         info(logger, "λ ≥ $(Float64(trunc(sgap,12)))")
+        info(logger, "λ($name, S) ≥ $(Float64(trunc(sgap,12)))")
         Kazhdan_κ = Kazhdan_from_sgap(sgap, length(S))
         Kazhdan_κ = Float64(trunc(Kazhdan_κ, 12))
         info(logger, "κ($name, S) ≥ $Kazhdan_κ: Group HAS property (T)!")
@@ -347,7 +192,50 @@ function check_property_T(name::String, S, Id, solver, upper_bound, tol, radius)
         return false
     end
 end
-   info(logger, "κ($name, S) ≥ $λ < 0: Tells us nothing about property (T)")
+
+function check_property_T(name::String, S, Id, solver, upper_bound, tol, radius)
+
+    isdir(name) || mkdir(name)
+    LOGGER = Memento.getlogger()
+
+    if exists(filename(name, :pm)) && exists(filename(name, :Δ))
+        # cached
+        info(LOGGER, "Loading precomputed Δ...")
+        Δ = Laplacian(name, parent(S[1]))
+    else
+        # compute
+        Δ = Laplacian(S, Id, LOGGER, radius=radius)
+        save(filename(name, :pm), "pm", parent(Δ).pm)
+        save(filename(name, :Δ), "Δ", Δ.coeffs)
+    end
+
+    fullpath = joinpath(name, string(upper_bound))
+    isdir(fullpath) || mkdir(fullpath)
+
+    if exists(filename(fullpath, :λ)) && exists(filename(fullpath, :P))
+        info(LOGGER, "Loading precomputed λ, P...")
+        λ, P = λandP(fullpath)
+    else
+        info(LOGGER, "Creating SDP problem...")
+        SDP_problem, varλ, varP = create_SDP_problem(Δ, constraints(parent(Δ).pm), upper_bound=upper_bound)
+        JuMP.setsolver(SDP_problem, solver)
+        info(LOGGER, Base.repr(SDP_problem))
+
+        @logtime LOGGER λ, P = λandP(fullpath, SDP_problem, varλ, varP)
+    end
+
+    info(LOGGER, "λ = $λ")
+    info(LOGGER, "sum(P) = $(sum(P))")
+    info(LOGGER, "maximum(P) = $(maximum(P))")
+    info(LOGGER, "minimum(P) = $(minimum(P))")
+
+    isapprox(eigvals(P), abs.(eigvals(P)), atol=tol) ||
+        warn("The solution matrix doesn't seem to be positive definite!")
+
+    if λ > 0
+        return check_λ(name, S, λ, P, radius, LOGGER)
+    end
+    info(LOGGER, "κ($name, S) ≥ $λ < 0: Tells us nothing about property (T)")
     return false
 end
 

src/SDPs.jl

@@ -13,7 +13,7 @@ function constraints(pm, total_length=maximum(pm))
     return constraints
 end
 
-function splaplacian(RG::GroupRing, S, T::Type=Float64)
+function spLaplacian(RG::GroupRing, S, T::Type=Float64)
     result = RG(T)
     result[RG.group()] = T(length(S))
     for s in S
@@ -22,7 +22,7 @@ function splaplacian(RG::GroupRing, S, T::Type=Float64)
     return result
 end
 
-function splaplacian{TT<:Ring}(RG::GroupRing{TT}, S, T::Type=Float64)
+function spLaplacian{TT<:Ring}(RG::GroupRing{TT}, S, T::Type=Float64)
     result = RG(T)
     result[one(RG.group)] = T(length(S))
     for s in S
@@ -55,21 +55,122 @@ function create_SDP_problem(Δ::GroupRingElem, matrix_constraints; upper_bound=I
     return m, λ, P
 end
 
-function solve_SDP(SDP_problem)
+function solve_SDP(m, varλ, varP; warmstart=nothing)
-    info(logger, Base.repr(SDP_problem))
 
-    o = redirect_stdout(solver_logger.handlers["solver_log"].io)
+    traits = JuMP.ProblemTraits(m, relaxation=false)
-    Base.Libc.flush_cstdio()
 
-    @logtime logger solution_status = MathProgBase.optimize!(SDP_problem.internalModel)
+    JuMP.build(m, traits=traits)
-    Base.Libc.flush_cstdio()
+    if warmstart != nothing
-
+        p_sol, d_sol, s = warmstart
-    redirect_stdout(o)
+        MathProgBase.SolverInterface.setwarmstart!(m.internalModel, p_sol; dual_sol = d_sol, slack=s);
-
-    if solution_status != :Optimal
-        warn(logger, "The solver did not solve the problem successfully!")
     end
-    info(logger, solution_status)
 
-    return 0
+    MathProgBase.optimize!(m.internalModel)
+
+    λ = MathProgBase.getobjval(m.internalModel)
+
+    warmstart = (m.internalModel.primal_sol, m.internalModel.dual_sol,
+          m.internalModel.slack)
+
+    fillfrominternal!(m, traits)
+
+    P = JuMP.getvalue(varP)
+    λ = JuMP.getvalue(varλ)
+
+    return λ, P, warmstart
+end
+
+function fillfrominternal!(m::JuMP.Model, traits)
+    # Copied from JuMP/src/solvers.jl:178
+
+    stat::Symbol = MathProgBase.status(m.internalModel)
+
+    numRows, numCols = length(m.linconstr), m.numCols
+    m.objBound = NaN
+    m.objVal = NaN
+    m.colVal = fill(NaN, numCols)
+    m.linconstrDuals = Array{Float64}(0)
+
+    discrete = (traits.int || traits.sos)
+
+    if stat == :Optimal
+        # If we think dual information might be available, try to get it
+        # If not, return an array of the correct length
+        if discrete
+            m.redCosts = fill(NaN, numCols)
+            m.linconstrDuals = fill(NaN, numRows)
+        else
+            if !traits.conic
+                m.redCosts = try
+                    MathProgBase.getreducedcosts(m.internalModel)[1:numCols]
+                catch
+                    fill(NaN, numCols)
+                end
+
+                m.linconstrDuals = try
+                    MathProgBase.getconstrduals(m.internalModel)[1:numRows]
+                catch
+                    fill(NaN, numRows)
+                end
+            elseif !traits.qp && !traits.qc
+                JuMP.fillConicDuals(m)
+            end
+        end
+    else
+        # Problem was not solved to optimality, attempt to extract useful
+        # information anyway
+
+        if traits.lin
+            if stat == :Infeasible
+                m.linconstrDuals = try
+                    infray = MathProgBase.getinfeasibilityray(m.internalModel)
+                    @assert length(infray) == numRows
+                    infray
+                catch
+                    suppress_warnings || warn("Infeasibility ray (Farkas proof) not available")
+                    fill(NaN, numRows)
+                end
+            elseif stat == :Unbounded
+                m.colVal = try
+                    unbdray = MathProgBase.getunboundedray(m.internalModel)
+                    @assert length(unbdray) == numCols
+                    unbdray
+                catch
+                    suppress_warnings || warn("Unbounded ray not available")
+                    fill(NaN, numCols)
+                end
+            end
+        end
+        # conic duals (currently, SOC and SDP only)
+        if !discrete && traits.conic && !traits.qp && !traits.qc
+            if stat == :Infeasible
+                JuMP.fillConicDuals(m)
+            end
+        end
+    end
+
+    # If the problem was solved, or if it terminated prematurely, try
+    # to extract a solution anyway. This commonly occurs when a time
+    # limit or tolerance is set (:UserLimit)
+    if !(stat == :Infeasible || stat == :Unbounded)
+        try
+            # 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
+        end
+        try
+            objVal = MathProgBase.getobjval(m.internalModel) + m.obj.aff.constant
+            colVal = MathProgBase.getsolution(m.internalModel)[1:numCols]
+            # Rescale off-diagonal terms of SDP variables
+            if traits.sdp
+                offdiagvars = JuMP.offdiagsdpvars(m)
+                colVal[offdiagvars] /= sqrt(2)
+            end
+            # Don't corrupt the answers if one of the above two calls fails
+            m.objVal = objVal
+            m.colVal = colVal
+        end
+    end
+
+    return stat
 end