Add JuMP interface #20
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,118 @@ | ||
| using Revise | ||
| using SparseArrays | ||
| using SDPLRPlus | ||
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| using LowRankOpt | ||
| using Dualization | ||
| include(joinpath(dirname(dirname(pathof(LowRankOpt))), "examples", "maxcut.jl")) | ||
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| n = 500 | ||
| import Random | ||
| Random.seed!(0) | ||
| W = sprand(n, n, 0.01) | ||
| W = W + W' | ||
| include(joinpath(@__DIR__, "problems.jl")) | ||
| C, As, b = maxcut(W) | ||
| @time sdplr(C, As, b, 1, maxmajoriter = 20); | ||
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| # SDPLRPlus does not support sparse factor | ||
| As = [SymLowRankMatrix(Diagonal(ones(1)), hcat(e_i(Float64, i, n, sparse = false))) for i in 1:n] | ||
| d = SDPLRPlus.SDPData(C, As, b) | ||
| var = SDPLRPlus.SolverVars(d, 1) | ||
| aux = SDPLRPlus.SolverAuxiliary(d) | ||
| @time sdplr(C, As, b, 1, maxmajoriter = 50); | ||
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| model = maxcut(W, dual_optimizer(LRO.Optimizer)) | ||
| set_attribute(model, "solver", LRO.BurerMonteiro.Solver) | ||
| set_attribute(model, "sub_solver", SDPLRPlus.Solver) | ||
| set_attribute(model, "ranks", [1]) | ||
| set_attribute(model, "maxmajoriter", 0) | ||
| set_attribute(model, "printlevel", 3) | ||
| @profview optimize!(model) | ||
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Owner
There was a problem hiding this comment. On this line I got one MKLSparseError, I'm quite confused about this error, do you have any clue? This error keeps showing up in the following benchmarking code too. |
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| solver = unsafe_backend(model).dual_problem.dual_model.model.optimizer.solver | ||
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| using BenchmarkTools | ||
| function A_sym_bench(aux, var, nlp, var_lro) | ||
| println("𝒜 sym") | ||
| @btime SDPLRPlus.𝒜!(var.primal_vio, aux, var.Rt) | ||
| @btime SDPLRPlus.𝒜!(var_lro.primal_vio, nlp, var_lro.Rt) | ||
| end | ||
| function A_not_sym_bench(aux, var, nlp, var_lro) | ||
| println("𝒜 not sym") | ||
| @btime SDPLRPlus.𝒜!(var.A_RD, aux, var.Rt, var.Gt) | ||
| @btime SDPLRPlus.𝒜!(var_lro.A_RD, nlp, var_lro.Rt, var_lro.Gt) | ||
| end | ||
| function At_bench(aux, var, nlp, var_lro) | ||
| println("𝒜t") | ||
| @btime SDPLRPlus.𝒜t!($var.Gt, $var.Rt, $aux, $var) | ||
| @btime SDPLRPlus.𝒜t!($var_lro.Gt, $var_lro.Rt, $nlp, $var_lro) | ||
| end | ||
| function At_bench2(aux, var, nlp, var_lro) | ||
| println("𝒜t rank-1") | ||
| x = rand(n) | ||
| y = similar(x) | ||
| @time SDPLRPlus.𝒜t!(y, aux, x, var) | ||
| @time SDPLRPlus.𝒜t!(y, nlp, x, var_lro) | ||
| end | ||
| nlp = solver.model | ||
| var_lro = solver.solver.var | ||
| A_sym_bench(aux, var, solver.model, solver.solver.var) | ||
| A_not_sym_bench(aux, var, solver.model, solver.solver.var) | ||
| At_bench(aux, var, solver.model, solver.solver.var) | ||
| At_bench2(aux, var, solver.model, solver.solver.var) | ||
| @profview SDPLRPlus.𝒜!(var.primal_vio, aux, var.Rt) | ||
| @profview for i in 1:100 | ||
| SDPLRPlus.𝒜!(var_lro.primal_vio, nlp, var_lro.Rt) | ||
| end | ||
| @time SDPLRPlus.𝒜!(var_lro.primal_vio, nlp, var_lro.Rt) | ||
| @time SDPLRPlus.𝒜t!(var_lro.Gt, var_lro.Rt, nlp, var_lro) | ||
| @profview for _ in 1:1000 | ||
| SDPLRPlus.𝒜t!(var_lro.Gt, var_lro.Rt, nlp, var_lro) | ||
| end | ||
| function bench_lmul(A) | ||
| n = LinearAlgebra.checksquare(A) | ||
| x = rand(1, n) | ||
| y = similar(x) | ||
| @profview for i in 1:100000 | ||
| LinearAlgebra.mul!(y, x, A, 2.0, 1.0) | ||
| end | ||
| #@btime LinearAlgebra.mul!($y, $x, $A, 2.0, 1.0) | ||
| end | ||
| function bench_rmul(A) | ||
| n = LinearAlgebra.checksquare(A) | ||
| x = rand(n, 1) | ||
| y = similar(x) | ||
| @btime LinearAlgebra.mul!($y, $A, $x, 2.0, 1.0) | ||
| end | ||
| bench_lmul(aux.symlowrank_As[1]); | ||
| A = aux.symlowrank_As[1] | ||
| n = LinearAlgebra.checksquare(A) | ||
| x = rand(1, n) | ||
| y = similar(x) | ||
| LinearAlgebra.mul!(y, x, A, 2.0, 1.0) | ||
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| bench_rmul(nlp.model.A[1]); | ||
| bench_lmul(nlp.model.A[1]) | ||
| A = nlp.model.A[1] | ||
| n = LinearAlgebra.checksquare(A) | ||
| x = rand(n, 1) | ||
| y = similar(x) | ||
| @time LinearAlgebra.mul!(y, A, x, 2.0, 1.0); | ||
| @btime LinearAlgebra.mul!($y, $A, $x, 2.0, 1.0); | ||
| methods(LinearAlgebra.mul!, typeof.((y, A, x, 2.0, 1.0))) | ||
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| A = nlp.model.A[1] | ||
| n = LinearAlgebra.checksquare(A) | ||
| x = rand(n) | ||
| y = similar(x) | ||
| x = rand(n) | ||
| y = similar(x) | ||
| @btime LinearAlgebra.mul!($y, $A, $x, 2.0, 1.0); | ||
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| C = LRO._lmul_diag!!(A.scaling, LRO.right_factor(A)' * x) | ||
| lA = LRO.left_factor(A) | ||
| @btime LinearAlgebra.mul!($y, $C, $lA) | ||
| @edit LinearAlgebra.mul!(y, lA, C); | ||
| @edit LinearAlgebra.mul!(y, lA, C, true, true); | ||
| @edit LinearAlgebra._rscale_add!(y, lA, C, true, true); | ||
| @edit LinearAlgebra.mul!($y, $lA, $C, 2.0, 1.0); | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -41,5 +41,6 @@ include("utils.jl") | |
| # main function | ||
| include("sdplr.jl") | ||
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| include("lowrankopt.jl") | ||
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| end | ||
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Running this bench.jl gives me the following error on this line.
I saw in
LowRankOpt.jl,vectordoes not have a default value, is this the cause? Function e_i in LowRankOpt.jl