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Split linear program and quad programs (#246)
* Split linear program and quad programs * Update linear_program.jl --------- Co-authored-by: Oscar Dowson <[email protected]>
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‎test/linear_program.jl

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# Copyright (c) 2020: Akshay Sharma and contributors
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#
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# Use of this source code is governed by an MIT-style license that can be found
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# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
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module TestLinearProgram
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using Test
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import DiffOpt
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import HiGHS
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import Ipopt
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import MathOptInterface as MOI
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import SCS
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const ATOL = 2e-4
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const RTOL = 2e-4
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function runtests()
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for name in names(@__MODULE__; all = true)
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if startswith("$name", "test_")
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@testset "$(name)" begin
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getfield(@__MODULE__, name)()
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end
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end
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end
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return
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end
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include(joinpath(@__DIR__, "utils.jl"))
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function test_differentiating_LP_checking_gradients_for_non_active_contraints()
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# Issue #40 from Gurobi.jl
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# min x
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# s.t. x >= 0
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# x >= 3
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nz = 1
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qp_test_with_solutions(
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HiGHS.Optimizer;
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q = ones(nz),
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G = -ones(2, nz),
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h = [0.0, -3.0],
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dzb = ones(nz),
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dqf = ones(nz),
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# Expected solutions
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dGb = [0.0, 3.0],
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dhb = [0.0, -1.0],
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)
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return
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end
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function test_differentiating_a_simple_LP_with_GreaterThan_constraint()
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# this is canonically same as above test
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# min x
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# s.t. x >= 3
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nz = 1
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qp_test_with_solutions(
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Ipopt.Optimizer;
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q = ones(nz),
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G = -ones(1, nz),
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h = [-3.0],
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dzb = ones(nz),
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dqf = ones(nz),
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# Expected solutions
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dGb = [3.0],
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dhb = [-1.0],
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)
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return
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end
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function test_differentiating_lp()
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# refered from - https://en.wikipedia.org/wiki/Simplex_algorithm#Example
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# max 2x + 3y + 4z
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# s.t. 3x+2y+z <= 10
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# 2x+5y+3z <= 15
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# x,y,z >= 0
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nz = 3
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qp_test_with_solutions(
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SCS.Optimizer;
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q = [-2.0, -3.0, -4.0],
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G = [
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3.0 2.0 1.0
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2.0 5.0 3.0
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-1.0 0.0 0.0
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0.0 -1.0 0.0
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0.0 0.0 -1.0
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],
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h = [10.0, 15.0, 0.0, 0.0, 0.0],
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dzb = ones(nz),
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dqf = ones(nz),
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# Expected solutions
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dqb = zeros(nz),
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dGb = [
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0.0 0.0 0.0
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0.0 0.0 -5/3
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0.0 0.0 5/3
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0.0 0.0 -10/3
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0.0 0.0 0.0
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],
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dhb = [0.0, 1 / 3, -1 / 3, 2 / 3, 0.0],
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)
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return
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end
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function test_differentiating_LP_with_variable_bounds()
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# max 2x + 3y + 4z
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# s.t. 3x+2y+z <= 10
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# 2x+5y+3z <= 15
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# x ≤ 3
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# 0 ≤ y ≤ 2
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# z ≥ 2
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# x,y,z >= 0
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# variant of previous test with same solution
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nz = 3
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qp_test_with_solutions(
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HiGHS.Optimizer;
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q = [-2.0, -3.0, -4.0],
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G = [
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3.0 2.0 1.0
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2.0 5.0 3.0
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-1.0 0.0 0.0
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0.0 -1.0 0.0
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0.0 0.0 -1.0
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1.0 0.0 0.0
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0.0 1.0 0.0
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0.0 0.0 1.0
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],
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h = [10.0, 15.0, 0.0, 0.0, 0.0, 3.0, 2.0, 6.0],
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dzb = ones(nz),
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dqf = ones(nz),
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# Expected solutions
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dqb = zeros(nz),
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dGb = [
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0.0 0.0 0.0
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0.0 0.0 -5/3
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0.0 0.0 5/3
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0.0 0.0 -10/3
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0.0 0.0 0.0
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0.0 0.0 0.0
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0.0 0.0 0.0
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0.0 0.0 0.0
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],
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dhb = [0.0, 1 / 3, -1 / 3, 2 / 3, 0.0, 0.0, 0.0, 0.0],
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)
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return
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end
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function test_differentiating_LP_with_variable_bounds_2()
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nz = 3
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qp_test_with_solutions(
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HiGHS.Optimizer;
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q = [-2.0, -3.0, -4.0],
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G = [
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3.0 2.0 1.0
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2.0 5.0 3.0
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0.0 -1.0 0.0
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0.0 0.0 -1.0
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],
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h = [10.0, 15.0, 0.0, 0.0],
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fix_indices = [1],
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fix_values = [0.0],
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dzb = ones(nz),
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dqf = ones(nz),
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# Expected solutions
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dqb = zeros(nz),
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dGb = [
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0.0 0.0 0.0
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0.0 0.0 -5/3
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0.0 0.0 -10/3
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0.0 0.0 0.0
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],
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dhb = [0.0, 1 / 3, 2 / 3, 0.0],
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dAb = [0.0 0.0 -5 / 3],
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dbb = [1 / 3],
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)
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return
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end
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"""
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max 2x + 3y + 4z
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s.t. 3x+2y+z <= 10
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2x+5y+3z <= 15
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x ≤ 3
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0 ≤ y ≤ 2
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6 ≥ z ≥ -1
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x, y, z >= 0
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variant of previous test with same solution
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"""
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function test_differentiating_lp_with_saf_with_le_ge_constraints()
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nz = 3
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qp_test_with_solutions(
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HiGHS.Optimizer;
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q = [-2.0, -3.0, -4.0],
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G = [
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3.0 2.0 1.0
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2.0 5.0 3.0
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-1.0 0.0 0.0
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0.0 -1.0 0.0
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0.0 0.0 -1.0
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],
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h = [10.0, 15.0, 0.0, 0.0, 0.0], #5
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ub_indices = [1, 2, 3],
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ub_values = [3.0, 2.0, 6.0],
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lb_indices = [1],
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lb_values = [-1.0],
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dzb = ones(nz),
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dqb = zeros(nz),
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dGb = [
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0.0 0.0 0.0
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0.0 0.0 -5/3
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0.0 0.0 5/3
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0.0 0.0 -10/3
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0.0 0.0 0.0
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0.0 0.0 0.0
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0.0 0.0 0.0
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0.0 0.0 0.0
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0.0 0.0 0.0
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],
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dhb = [0.0, 1 / 3, -1 / 3, 2 / 3, 0.0, 0.0, 0.0, 0.0, 0.0],
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)
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return
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end
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function test_differentiating_lp_with_nonactive_constraints()
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# Issue #40 from Gurobi.jl
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# min x
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# s.t. x >= 0
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# x >= 3
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qp_test_with_solutions(
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HiGHS.Optimizer;
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q = [1.0],
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G = -ones(2, 1),
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h = [0.0, -3.0],
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dzb = -ones(1),
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dhf = [0.0, 1.0],
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# Expected solutions
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z = [3.0],
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λ = [0.0, 1.0],
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dzf = -ones(1),
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dλb = zeros(2),
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dhb = [0.0, 1.0],
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∇zb = zeros(1),
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∇λb = [0.0, -1.0],
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dλf = zeros(2),
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)
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return
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end
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end # module
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TestLinearProgram.runtests()

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