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Replace random ElasticNet problem in tests with fixed one #38

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Merged
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May 3, 2020
Merged

Replace random ElasticNet problem in tests with fixed one #38

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62 changes: 38 additions & 24 deletions test/problems/test_elasticnet.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -3,16 +3,14 @@
using ProximalOperators
using ProximalAlgorithms
using LinearAlgebra
using Random

Random.seed!(0)
A = T[ 1.0 -2.0 3.0 -4.0 5.0;
2.0 -1.0 0.0 -1.0 3.0;
-1.0 0.0 4.0 -3.0 2.0;
-1.0 -1.0 -1.0 1.0 3.0]
b = T[1.0, 2.0, 3.0, 4.0]

# TODO: generate problem with known solution instead

m, n = 50, 200

A = randn(T, m, n)
b = randn(T, m)
m, n = size(A)

R = real(T)

Expand All @@ -23,11 +21,11 @@

L = opnorm(A)^2

x0 = zeros(T, n)
solver = ProximalAlgorithms.PANOC{R}(tol=eps(R))
x_panoc, it = solver(x0, f=loss, A=A, g=reg)
x_star = T[-0.6004983388704322, 0.0, 0.0, 0.195182724252491, 0.764119601328903]

@testset "DavisYin" begin

# with known initial iterate

x0 = zeros(T, n)
solver = ProximalAlgorithms.DavisYin{R}(tol=R(1e-6))
Expand All @@ -36,9 +34,11 @@
)
@test eltype(xf_dys) == T
@test eltype(xg_dys) == T
@test norm(xf_dys - x_panoc, Inf) <= 1e-3
@test norm(xg_dys - x_panoc, Inf) <= 1e-3
@test it_dys <= 1900
@test norm(xf_dys - x_star, Inf) <= 1e-3
@test norm(xg_dys - x_star, Inf) <= 1e-3
@test it_dys <= 140

# with random initial iterate

x0 = randn(T, n)
solver = ProximalAlgorithms.DavisYin{R}(tol=R(1e-6))
Expand All @@ -47,21 +47,36 @@
)
@test eltype(xf_dys) == T
@test eltype(xg_dys) == T
@test norm(xf_dys - x_panoc, Inf) <= 1e-3
@test norm(xg_dys - x_panoc, Inf) <= 1e-3
@test it_dys <= 2600
@test norm(xf_dys - x_star, Inf) <= 1e-3
@test norm(xg_dys - x_star, Inf) <= 1e-3

end

afba_test_params = [
(R(2), R(0), 230),
(R(1), R(1), 230),
(R(0), R(1), 470),
(R(0), R(0), 500),
(R(1), R(0), 230)
(R(2), R(0), 130),
(R(1), R(1), 120),
(R(0), R(1), 320),
(R(0), R(0), 160),
(R(1), R(0), 130)
]

@testset "AFBA" for (theta, mu, maxit) in afba_test_params

# with known initial iterates

x0 = zeros(T, n)
y0 = zeros(T, m)

solver = ProximalAlgorithms.AFBA{R}(theta=theta, mu=mu, tol=R(1e-6))
x_afba, y_afba, it_afba = solver(
x0, y0, f=reg2, g=reg1, h=loss, L=A, betaQ=R(1),
)
@test eltype(x_afba) == T
@test eltype(y_afba) == T
@test norm(x_afba - x_star, Inf) <= 1e-4
@test it_afba <= maxit

# with random initial iterates

x0 = randn(T, n)
y0 = randn(T, m)
Expand All @@ -72,8 +87,7 @@
)
@test eltype(x_afba) == T
@test eltype(y_afba) == T
@test norm(x_afba - x_panoc, Inf) <= 1e-4
@test it_afba <= maxit
@test norm(x_afba - x_star, Inf) <= 1e-4

end
end