Implement _add_pwl_constraints

master
Alinson S. Xavier 1 week ago
parent f4e97ff7f2
commit 744b043461

@ -14,7 +14,7 @@ function fix(x::Float64, v::Float64; force)
return abs(x - v) < 1e-6 || error("Value mismatch: $x != $v")
end
function set_name(x::Number, n::String)
function set_name(::Number, ::String)
# nop
end
@ -45,3 +45,76 @@ function _set_names!(dict::Dict)
end
end
end
"""
_add_pwl_constraints(model, xvar, yvars, xpts, ypts)
Add piecewise-linear constraints to a JuMP model for multiple y variables.
Creates constraints y_i = f_i(x) where each f_i is a piecewise-linear function
defined by the breakpoints (xpts, ypts[:, i]).
# Arguments
- `model`: JuMP model
- `xvar`: The x variable (JuMP variable)
- `yvars`: Vector of y variables (JuMP variables)
- `xpts`: Vector of x values for breakpoints (must be in non-decreasing order)
- `ypts`: Matrix of y values where ypts[i, j] is the y value for the j-th variable
at the i-th breakpoint
# Example
```julia
@variable(model, y1)
@variable(model, y2)
ypts_matrix = [1.5 2.0; 0.0 1.5; 3.0 0.5] # 3 breakpoints, 2 y variables
_add_pwl_constraints(model, x, [y1, y2], [0.0, 1.0, 2.0], ypts_matrix, name="multiPWL")
```
"""
function _add_pwl_constraints(model, xvar, yvars, xpts, ypts)
# Input validation
ypts isa AbstractMatrix || throw(ArgumentError("ypts must be a matrix"))
length(xpts) == size(ypts, 1) ||
throw(ArgumentError("xpts length must match number of rows in ypts"))
length(yvars) == size(ypts, 2) ||
throw(ArgumentError("Number of y variables must match number of columns in ypts"))
length(xpts) >= 1 || throw(ArgumentError("At least one breakpoint is required"))
# Check that xpts is increasing
for i = 2:length(xpts)
xpts[i] > xpts[i-1] || throw(ArgumentError("xpts must be in increasing order"))
end
n_points = length(xpts)
n_yvars = length(yvars)
if n_points == 1
# Single point case: y_j = ypts[1,j], x = xpts[1]
@constraint(model, xvar == xpts[1])
for j = 1:n_yvars
@constraint(model, yvars[j] == ypts[1, j])
end
elseif n_points == 2
# Two points case: single linear segment for each y variable
x1, x2 = xpts[1], xpts[2]
# Linear relationship for each y variable: y_j = y1_j + slope_j * (x-x1)
for j = 1:n_yvars
y1, y2 = ypts[1, j], ypts[2, j]
slope = (y2 - y1) / (x2 - x1)
@constraint(model, yvars[j] == y1 + slope * (xvar - x1))
end
else
# Multiple segments case (3+ points): use SOS2 formulation
λ = @variable(model, [1:n_points], lower_bound = 0, upper_bound = 1)
@constraint(model, λ in SOS2())
@constraint(model, sum(λ) == 1)
@constraint(model, xvar == sum(xpts[i] * λ[i] for i = 1:n_points))
for j = 1:n_yvars
@constraint(model, yvars[j] == sum(ypts[i, j] * λ[i] for i = 1:n_points))
end
end
return
end

@ -7,6 +7,7 @@ using JuliaFormatter
include("instance/parse_test.jl")
include("model/build_test.jl")
include("model/dist_test.jl")
include("model/jumpext_test.jl")
include("reports_test.jl")
include("../fixtures/boat_example.jl")
@ -23,6 +24,7 @@ function runtests()
model_build_test()
model_dist_test()
report_tests()
jumpext_test()
end
return
end

@ -0,0 +1,144 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using RELOG
using JuMP
using HiGHS
using Test
function jumpext_test()
jumpext_pwl_single_point()
jumpext_pwl_two_points()
jumpext_pwl_multiple_points()
jumpext_pwl_input_validation()
return
end
function jumpext_pwl_single_point()
model = Model(HiGHS.Optimizer)
set_silent(model)
@variable(model, x)
@variable(model, y1)
@variable(model, y2)
xpts = [5.0]
ypts = [10.0 20.0]
RELOG._add_pwl_constraints(model, x, [y1, y2], xpts, ypts)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(x) 5.0 atol = 1e-6
@test value(y1) 10.0 atol = 1e-6
@test value(y2) 20.0 atol = 1e-6
return
end
function jumpext_pwl_two_points()
model = Model(HiGHS.Optimizer)
set_silent(model)
@variable(model, x)
@variable(model, y1)
@variable(model, y2)
xpts = [0.0, 2.0]
ypts = [0.0 10.0; 4.0 6.0]
RELOG._add_pwl_constraints(model, x, [y1, y2], xpts, ypts)
# Test at x = 1
JuMP.fix(x, 1.0)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(y1) 2.0 atol = 1e-6
@test value(y2) 8.0 atol = 1e-6
# Test at x = 2
JuMP.fix(x, 2.0)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(y1) 4.0 atol = 1e-6
@test value(y2) 6.0 atol = 1e-6
return
end
function jumpext_pwl_multiple_points()
model = Model(HiGHS.Optimizer)
set_silent(model)
@variable(model, x)
@variable(model, y1)
@variable(model, y2)
xpts = [0.0, 1.0, 2.0]
ypts = [0.0 5.0; 2.0 3.0; 1.0 4.0]
RELOG._add_pwl_constraints(model, x, [y1, y2], xpts, ypts)
# Test at x = 0.5
JuMP.fix(x, 0.5)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(y1) 1.0 atol = 1e-6
@test value(y2) 4.0 atol = 1e-6
# Test at x = 1
JuMP.fix(x, 1.0)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(y1) 2.0 atol = 1e-6
@test value(y2) 3.0 atol = 1e-6
# Test at x = 1.5
JuMP.fix(x, 1.5)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(y1) 1.5 atol = 1e-6
@test value(y2) 3.5 atol = 1e-6
return
end
function jumpext_pwl_input_validation()
model = Model(HiGHS.Optimizer)
@variable(model, x)
@variable(model, y)
# Test non-matrix ypts
@test_throws ArgumentError RELOG._add_pwl_constraints(model, x, [y], [1.0], [1.0])
# Test mismatched dimensions
@test_throws ArgumentError RELOG._add_pwl_constraints(
model,
x,
[y],
[1.0, 2.0],
[1.0 2.0],
)
@test_throws ArgumentError RELOG._add_pwl_constraints(
model,
x,
[y],
[1.0],
[1.0 2.0; 3.0 4.0],
)
# Test empty breakpoints
@test_throws ArgumentError RELOG._add_pwl_constraints(
model,
x,
[y],
Float64[],
Matrix{Float64}(undef, 0, 1),
)
# Test non-increasing x points
@test_throws ArgumentError RELOG._add_pwl_constraints(
model,
x,
[y],
[2.0, 1.0],
[1.0; 2.0],
)
@test_throws ArgumentError RELOG._add_pwl_constraints(
model,
x,
[y],
[1.0, 1.0],
[1.0; 2.0],
)
return
end
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