https://github.com/ds4dm/Tulip.jl
Tip revision: 070a9f4dff3bad91bd62fcac11e0b9e84da94df2 authored by mtanneau on 26 June 2021, 22:55:46 UTC
Add primal and dual tolerances in presolve (#100)
Add primal and dual tolerances in presolve (#100)
Tip revision: 070a9f4
MPC.jl
function run_tests_mpc(T::Type)
Tv = Vector{T}
params = TLP.IPMOptions{T}()
kkt_options = TLP.KKTOptions{T}()
@testset "step length" begin
m, n, p = 2, 2, 1
pt = TLP.Point{T, Tv}(m, n, p, hflag=false)
pt.x .= one(T)
pt.xl .= one(T)
pt.xu .= one(T)
pt.y .= zero(T)
pt.zl .= zero(T)
pt.zu .= zero(T)
pt.τ = one(T)
pt.κ = one(T)
pt.μ = one(T)
d = TLP.Point{T, Tv}(m, n, p, hflag=false)
d.x .= one(T)
d.xl .= one(T)
d.xu .= one(T)
d.y .= zero(T)
d.zl .= zero(T)
d.zu .= zero(T)
d.τ = one(T)
d.κ = one(T)
d.μ = one(T)
# Max step length for a single (x, d)
@inferred TLP.max_step_length(pt.x, d.x)
@test TLP.max_step_length(ones(T, 1), ones(T, 1)) == T(Inf)
@test TLP.max_step_length(ones(T, 1), -ones(T, 1)) ≈ one(T)
@test TLP.max_step_length(zeros(T, 1), -ones(T, 1)) ≈ zero(T)
@test TLP.max_step_length(zeros(T, 1), ones(T, 1)) == T(Inf)
# Max step length for the whole primal-dual point
@inferred TLP.max_step_length(pt, d)
@test TLP.max_step_length(pt, d) ≈ one(T)
end
# Simple example:
#=
min x1 - x2
s.τ. x1 + x2 = 1
x1 - x2 = 0
0 <= x1 <= 2
0 <= x2 <= 2
=#
m, n = 2, 2
p = 2 * n
A = Matrix{T}([
[1 1];
[1 -1]
])
b = Vector{T}([1, 0])
c = Vector{T}([1, -1])
c0 = zero(T)
l = Vector{T}([0, 0])
u = Vector{T}([2, 2])
dat = Tulip.IPMData(A, b, true, c, c0, l, u)
ipm = TLP.MPC(dat, kkt_options)
# Primal-dual optimal solution
# x1 = x2 = 0.5; xl = 0.5; xu = 1.5; τ = 1
# y1 = 0, y2 = 1; zl = zu = 0; κ = 0
ipm.pt.x .= T.([1 // 2, 1 // 2])
ipm.pt.xl .= T.([1 // 2, 1 // 2])
ipm.pt.xu .= T.([3 // 2, 3 // 2])
ipm.pt.y .= T.([0, 1])
ipm.pt.zl .= T.([0, 0])
ipm.pt.zu .= T.([0, 0])
ipm.pt.τ = 1
ipm.pt.κ = 0
ipm.pt.μ = 0
ϵ = sqrt(eps(T))
@testset "Residuals" begin
@inferred TLP.compute_residuals!(ipm)
TLP.compute_residuals!(ipm)
@test isapprox(ipm.res.rp_nrm, zero(T); atol=ϵ, rtol=ϵ)
@test isapprox(ipm.res.ru_nrm, zero(T); atol=ϵ, rtol=ϵ)
@test isapprox(ipm.res.rd_nrm, zero(T); atol=ϵ, rtol=ϵ)
@test isapprox(ipm.res.rg_nrm, zero(T); atol=ϵ, rtol=ϵ)
end
@testset "Convergence" begin
ipm.solver_status = TLP.Trm_Unknown
TLP.update_solver_status!(ipm, ϵ, ϵ, ϵ, ϵ)
@test ipm.solver_status == TLP.Trm_Optimal
# TODO: dual infeasible
# TODO: primal infeasible
# TODO: ill-posed
end
end
@testset "MPC" begin
for T in TvTYPES
@testset "$T" begin run_tests_mpc(T) end
end
end