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Split linear program and quad programs #246

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0093b04
Split linear program and quad programs
blegat Apr 12, 2023
8af7139
Update linear_program.jl
odow Apr 13, 2023
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251 changes: 251 additions & 0 deletions test/linear_program.jl
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# Copyright (c) 2020: Akshay Sharma and contributors
#
# Use of this source code is governed by an MIT-style license that can be found
# in the LICENSE.md file or at https://opensource.org/licenses/MIT.

module TestLinearProgram

using Test
import DelimitedFiles
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import DiffOpt
import HiGHS
import Ipopt
import MathOptInterface as MOI
import SCS

const ATOL = 2e-4
const RTOL = 2e-4

function runtests()
for name in names(@__MODULE__; all = true)
if startswith("$name", "test_")
@testset "$(name)" begin
getfield(@__MODULE__, name)()
end
end
end
return
end

include(joinpath(@__DIR__, "utils.jl"))

function test_differentiating_LP_checking_gradients_for_non_active_contraints()
# Issue #40 from Gurobi.jl
# min x
# s.t. x >= 0
# x >= 3
nz = 1
qp_test_with_solutions(
HiGHS.Optimizer;
q = ones(nz),
G = -ones(2, nz),
h = [0.0, -3.0],
dzb = ones(nz),
dqf = ones(nz),
# Expected solutions
dGb = [0.0, 3.0],
dhb = [0.0, -1.0],
)
return
end

function test_differentiating_a_simple_LP_with_GreaterThan_constraint()
# this is canonically same as above test
# min x
# s.t. x >= 3
nz = 1
qp_test_with_solutions(
Ipopt.Optimizer;
q = ones(nz),
G = -ones(1, nz),
h = [-3.0],
dzb = ones(nz),
dqf = ones(nz),
# Expected solutions
dGb = [3.0],
dhb = [-1.0],
)
return
end

function test_differentiating_lp()
# refered from - https://en.wikipedia.org/wiki/Simplex_algorithm#Example
# max 2x + 3y + 4z
# s.t. 3x+2y+z <= 10
# 2x+5y+3z <= 15
# x,y,z >= 0
nz = 3
qp_test_with_solutions(
SCS.Optimizer;
q = [-2.0, -3.0, -4.0],
G = [
3.0 2.0 1.0
2.0 5.0 3.0
-1.0 0.0 0.0
0.0 -1.0 0.0
0.0 0.0 -1.0
],
h = [10.0, 15.0, 0.0, 0.0, 0.0],
dzb = ones(nz),
dqf = ones(nz),
# Expected solutions
dqb = zeros(nz),
dGb = [
0.0 0.0 0.0
0.0 0.0 -5/3
0.0 0.0 5/3
0.0 0.0 -10/3
0.0 0.0 0.0
],
dhb = [0.0, 1 / 3, -1 / 3, 2 / 3, 0.0],
)
return
end

function test_differentiating_LP_with_variable_bounds()
# max 2x + 3y + 4z
# s.t. 3x+2y+z <= 10
# 2x+5y+3z <= 15
# x ≤ 3
# 0 ≤ y ≤ 2
# z ≥ 2
# x,y,z >= 0
# variant of previous test with same solution
nz = 3
qp_test_with_solutions(
HiGHS.Optimizer;
q = [-2.0, -3.0, -4.0],
G = [
3.0 2.0 1.0
2.0 5.0 3.0
-1.0 0.0 0.0
0.0 -1.0 0.0
0.0 0.0 -1.0
1.0 0.0 0.0
0.0 1.0 0.0
0.0 0.0 1.0
],
h = [10.0, 15.0, 0.0, 0.0, 0.0, 3.0, 2.0, 6.0],
dzb = ones(nz),
dqf = ones(nz),
# Expected solutions
dqb = zeros(nz),
dGb = [
0.0 0.0 0.0
0.0 0.0 -5/3
0.0 0.0 5/3
0.0 0.0 -10/3
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
],
dhb = [0.0, 1 / 3, -1 / 3, 2 / 3, 0.0, 0.0, 0.0, 0.0],
)
return
end

function test_differentiating_LP_with_variable_bounds_2()
nz = 3
qp_test_with_solutions(
HiGHS.Optimizer;
q = [-2.0, -3.0, -4.0],
G = [
3.0 2.0 1.0
2.0 5.0 3.0
0.0 -1.0 0.0
0.0 0.0 -1.0
],
h = [10.0, 15.0, 0.0, 0.0],
fix_indices = [1],
fix_values = [0.0],
dzb = ones(nz),
dqf = ones(nz),
# Expected solutions
dqb = zeros(nz),
dGb = [
0.0 0.0 0.0
0.0 0.0 -5/3
0.0 0.0 -10/3
0.0 0.0 0.0
],
dhb = [0.0, 1 / 3, 2 / 3, 0.0],
dAb = [0.0 0.0 -5 / 3],
dbb = [1 / 3],
)
return
end

"""
max 2x + 3y + 4z
s.t. 3x+2y+z <= 10
2x+5y+3z <= 15
x ≤ 3
0 ≤ y ≤ 2
6 ≥ z ≥ -1
x, y, z >= 0
variant of previous test with same solution
"""
function test_differentiating_lp_with_saf_with_le_ge_constraints()
nz = 3
qp_test_with_solutions(
HiGHS.Optimizer;
q = [-2.0, -3.0, -4.0],
G = [
3.0 2.0 1.0
2.0 5.0 3.0
-1.0 0.0 0.0
0.0 -1.0 0.0
0.0 0.0 -1.0
],
h = [10.0, 15.0, 0.0, 0.0, 0.0], #5
ub_indices = [1, 2, 3],
ub_values = [3.0, 2.0, 6.0],
lb_indices = [1],
lb_values = [-1.0],
dzb = ones(nz),
dqb = zeros(nz),
dGb = [
0.0 0.0 0.0
0.0 0.0 -5/3
0.0 0.0 5/3
0.0 0.0 -10/3
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
],
dhb = [0.0, 1 / 3, -1 / 3, 2 / 3, 0.0, 0.0, 0.0, 0.0, 0.0],
)
return
end

function test_differentiating_lp_with_nonactive_constraints()
# Issue #40 from Gurobi.jl
# min x
# s.t. x >= 0
# x >= 3
qp_test_with_solutions(
HiGHS.Optimizer;
q = [1.0],
G = -ones(2, 1),
h = [0.0, -3.0],
dzb = -ones(1),
dhf = [0.0, 1.0],
# Expected solutions
z = [3.0],
λ = [0.0, 1.0],
dzf = -ones(1),
dλb = zeros(2),
dhb = [0.0, 1.0],
∇zb = zeros(1),
∇λb = [0.0, -1.0],
dλf = zeros(2),
)
return
end

end # module

TestLinearProgram.runtests()
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