PiecewiseLinearOpt.jl

PiecewiseLinearOpt.jl is a JuMP extension for modeling optimization problems containing piecewise linear functions.

This package is an accompaniment to a paper entitled Nonconvex piecewise linear functions: Advanced formulations and simple modeling tools, by Joey Huchette and Juan Pablo Vielma.

If you need help, please ask a question on the JuMP community forum.

If you have a reproducible example of a bug, please open a GitHub issue.

PiecewiseLinearOpt.jl is licensed under the MIT license.

Install PiecewiseLinearOpt using Pkg.add:

import Pkg
Pkg.add("PiecewiseLinearOpt")

Current support is limited to modeling the graph of a continuous piecewise linear function, either univariate or bivariate, with the goal of adding support for the epigraphs of lower semicontinuous piecewise linear functions.

Consider a piecewise linear function f. The function is described a domain d, which is a set of breakpoints between pieces, and the function value fd at those breakpoints:

julia> f(x) = sin(x)
f (generic function with 1 method)

julia> d = 0:0.5:2pi
0.0:0.5:6.0

julia> fd = f.(d)
13-element Vector{Float64}:
  0.0
  0.479425538604203
  0.8414709848078965
  0.9974949866040544
  0.9092974268256817
  0.5984721441039564
  0.1411200080598672
 -0.35078322768961984
 -0.7568024953079282
 -0.977530117665097
 -0.9589242746631385
 -0.7055403255703919
 -0.27941549819892586

To represent this function in a JuMP model, do:

using JuMP, PiecewiseLinearOpt
model = Model()
@variable(model, x)
z = PiecewiseLinearOpt.piecewiselinear(model, x, d, fd; method = :CC)
@objective(model, Min, z) # minimize f(x)

Consider piecewise linear approximation for the function $f(x, y) = exp(x + y)$:

using JuMP, PiecewiseLinearOpt
model = Model()
@variable(model, x)
@variable(model, y)
z = PiecewiseLinearOpt.piecewiselinear(
    model,
    x,
    y,
    0:0.1:1,
    0:0.1:1,
    (u, v) -> exp(u + v);
    method = :DisaggLogarithmic,
)
@objective(model, Min, z)

Supported univariate formulations:

• Convex combination (:CC)
• Multiple choice (:MC)
• Native SOS2 branching (:SOS2)
• Incremental (:Incremental)
• Logarithmic (:Logarithmic; default)
• Disaggregated Logarithmic (:DisaggLogarithmic)
• Binary zig-zag (:ZigZag)
• General integer zig-zag (:ZigZagInteger)

Supported bivariate formulations for entire constraint:

• Convex combination (:CC)
• Multiple choice (:MC)
• Disaggregated Logarithmic (:DisaggLogarithmic)

Also, you can use any univariate formulation for bivariate functions as well. They will be used to impose two axis-aligned SOS2 constraints, along with the "6-stencil" formulation for the triangle selection portion of the constraint. See the associated paper for more details. In particular, the following are also acceptable bivariate formulation choices:

• Native SOS2 branching (:SOS2)
• Incremental (:Incremental)
• Logarithmic (:Logarithmic)
• Binary zig-zag (:ZigZag)
• General integer zig-zag (:ZigZagInteger)