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use MKLDirect for G₂
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@ -1,6 +1,6 @@
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using LinearAlgebra
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BLAS.set_num_threads(8)
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using MKL_jll
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ENV["OMP_NUM_THREADS"] = 4
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using Groups
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@ -84,16 +84,59 @@ unit = Δ
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warm = nothing
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solve_in_loop(
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model,
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wd,
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varP;
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logdir = "./log/G2/r=$HALFRADIUS/Adj-InfΔ",
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optimizer = scs_optimizer(;
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eps = 1e-10,
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max_iters = 50_000,
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accel = 50,
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alpha = 1.95,
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),
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data = (elt = elt, unit = unit, halfradius = HALFRADIUS),
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)
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let status = JuMP.OPTIMIZE_NOT_CALLED, warm = warm, eps = 1e-9
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certified, λ = false, 0.0
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while status ≠ JuMP.OPTIMAL
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@time status, warm = PropertyT.solve(
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model,
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scs_optimizer(;
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linear_solver = SCS.MKLDirectSolver,
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eps = eps,
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max_iters = 100_000,
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accel = 50,
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alpha = 1.95,
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),
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warm,
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)
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@info "reconstructing the solution"
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Q = @time let wd = wd, Ps = [JuMP.value.(P) for P in varP], eps = eps
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PropertyT.__droptol!.(Ps, 100eps)
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Qs = real.(sqrt.(Ps))
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PropertyT.__droptol!.(Qs, eps)
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PropertyT.reconstruct(Qs, wd)
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end
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@info "certifying the solution"
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@time certified, λ = PropertyT.certify_solution(
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elt,
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unit,
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JuMP.objective_value(model),
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Q;
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halfradius = HALFRADIUS,
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augmented = true,
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)
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end
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if certified && λ > 0
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Κ(λ, S) = round(sqrt(2λ / length(S)), Base.RoundDown; digits = 5)
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@info "Certified result: $G has property (T):" N λ Κ(λ, S)
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else
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@info "Could NOT certify the result:" certified λ
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end
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end
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# solve_in_loop(
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# model,
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# wd,
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# varP;
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# logdir = "./log/G2/r=$HALFRADIUS/Adj-InfΔ",
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# optimizer = scs_optimizer(;
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# eps = 1e-10,
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# max_iters = 50_000,
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# accel = 50,
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# alpha = 1.95,
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# ),
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# data = (elt = elt, unit = unit, halfradius = HALFRADIUS),
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# )
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