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HiGHS.jl

HiGHS.jl is a Julia package that interfaces with HiGHS.

HiGHS.jl has two components:

The C API can be accessed via HiGHS.Highs_xxx functions, where the names and arguments are identical to the C API.

Installation

Install HiGHS as follows:

import Pkg
Pkg.add("HiGHS")

In addition to installing the HiGHS.jl package, this command will also download and install the HiGHS binaries. (You do not need to install or compile HiGHS separately.)

To use a custom binary, read the Custom solver binaries section of the JuMP documentation.

Use with JuMP

Pass HiGHS.Optimizer to JuMP.Model to create a JuMP model with HiGHS as the optimizer. Set options using set_optimizer_attribute.

using JuMP
import HiGHS
model = Model(HiGHS.Optimizer)
set_optimizer_attribute(model, "presolve", "on")
set_optimizer_attribute(model, "time_limit", 60.0)

For more details, including a range of tutorials and examples using HiGHS, see the JuMP documentation.

Issues and feedback

HiGHS.jl is maintained by the JuMP community and is not officially maintained or supported by the HiGHS developers.

To report a problem (e.g., incorrect results, or a crash of the solver), or make a suggestion for how to improve HiGHS.jl, please file a GitHub issue at HiGHS.jl.

If you use HiGHS from JuMP, use JuMP.write_to_file(model, "filename.mps") to write your model an MPS file, then upload the MPS file to https://gist.github.com and provide a link to the gist in the GitHub issue.

C API

HiGHS.jl is a thin wrapper around the complete HiGHS C API.

As a basic example, we solve the model:

\[\begin{aligned} \min \quad & x + y \\ \textrm{subject to} \quad & 5 \le x + 2y \le 15 \\ & 6 \le 3x + 2y \\ & 0 \le x \le 4 \\ & 1 \le y \\ & y \in \mathbb{Z}. \end{aligned}\]

Here is the corresponding Julia code:

julia> using HiGHS

julia> highs = Highs_create()
Ptr{Nothing} @0x00007fc4557d3200

julia> ret = Highs_setBoolOptionValue(highs, "log_to_console", false)
0

julia> @assert ret == 0  # If ret != 0, something went wrong

julia> Highs_addCol(highs, 1.0, 0.0, 4.0, 0, C_NULL, C_NULL)   # x is column 0
0

julia> Highs_addCol(highs, 1.0, 1.0, Inf, 0, C_NULL, C_NULL)   # y is column 1
0

julia> Highs_changeColIntegrality(highs, 1, kHighsVarTypeInteger)
0

julia> Highs_changeObjectiveSense(highs, kHighsObjSenseMinimize)
0

julia> senseP = Ref{Cint}(0)  # Instead of passing `&sense`, pass a Julia `Ref`
Base.RefValue{Int32}(0)

julia> Highs_getObjectiveSense(model, senseP)
0

julia> senseP[] == kHighsObjSenseMinimize  # Dereference with senseP[]
true

julia> Highs_addRow(highs, 5.0, 15.0, 2, Cint[0, 1], [1.0, 2.0])
0

julia> Highs_addRow(highs, 6.0, Inf, 2, Cint[0, 1], [3.0, 2.0])
0

julia> Highs_run(highs)
0

julia> col_value = zeros(Cdouble, 2);

julia> Highs_getSolution(highs, col_value, C_NULL, C_NULL, C_NULL)
0

julia> col_value
2-element Vector{Float64}:
 1.0
 2.0

julia> Highs_destroy(highs)