#linear-programming #solver #optimization #math #linker

sys highs-sys

Rust binding for the HiGHS linear programming solver. See http://highs.dev.

20 releases (7 stable)

1.6.2 Feb 21, 2024
1.6.0 Sep 14, 2023
1.5.3 Jun 30, 2023
1.5.0 Jan 18, 2023
0.1.4 Mar 16, 2021

#1427 in Algorithms

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646 downloads per month
Used in 4 crates (via highs)

MIT license

94K SLoC

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docs.rs badge rust crate

Rust binding for the HiGHS linear programming solver. See http://highs.dev.

This repository contains the source for HiGHS itself as a submodule. You should clone it with

git clone --recursive git@github.com:rust-or/highs-sys.git

This crate can either use and link a version of HiGHS that is already installed and available on your system or build and statically link HiGHS itself.


At runtime, HiGHS depends at the minimum on the C++ standard library. It needs to be installed both on your system and any system you want to deploy your application to.

How you install these depends on your operating system.


sudo apt-get install libstdc++6

(but it is probably already installed on your system)


libc++ comes by default when installing XCode.

Building HiGHS

This crate can either build HiGHS itself and link it statically or link against an already installed version. To build HiGHS, you need at least a C++ compiler and cmake. Enabling additional features may incur additional runtime dependencies.


These can be easily installed using your distribution's package manager. For example, on Debian: sudo apt install g++ cmake.


To install a C++ compiler, run xcode-select --install. The easiest way to obtain cmake is via brew: brew install cmake.

If you enable the libz or ninja features, you should also install these via brew.


You need to install CMake and Clang (available in LLVM).

They are available in winget.

winget install -e --id Kitware.CMake
winget install -e --id LLVM.LLVM

If you enable the Ninja feature, you can also obtain Ninja from winget:

winget install -e --id Ninja-build.Ninja

If desired, libz needs to be installed and made discoverable by setting the ZLIB_ROOT environment variable.

Using a pre-installed version of HiGHS

Rather than building HiGHS, you can link against a version you have already installed on your system. To do that, install pkg-config on your system and enable the discover feature on this crate.

This will generally cause HiGHS to be linked dynamically, which means it also needs to be installed on the system you deploy to.

Note that at the time of writing, HiGHS is packaged in few package managers, so you may need to build and install HiGHS from source.

Feature Flags

build (enabled by default): build HiGHS and link it statically highs_release: set CMake profile to "Release" regardless of build profile; only takes effect when build is enabled. libz: enable HiGHS libz linking to enable support for reading 'mps.gz'; only takes effect when build is enabled. ninja: set CMake generator to Ninja; only takes effect when build is enabled. discover: use pkg-config to discover and link against an already installed version of HiGHS; takes precedence over build if both are enabled


    // This illustrates the use of Highs_call, the simple C interface to
// HiGHS. It's designed to solve the general LP problem
// Min c^Tx subject to L <= Ax <= U; l <= x <= u
// where A is a matrix with m rows and n columns
// The scalar n is numcol
// The scalar m is numrow
// The vector c is colcost
// The vector l is collower
// The vector u is colupper
// The vector L is rowlower
// The vector U is rowupper
// The matrix A is represented in packed column-wise form: only its
// nonzeros are stored
// * The number of nonzeros in A is nnz
// * The row indices of the nonnzeros in A are stored column-by-column
// in aindex
// * The values of the nonnzeros in A are stored column-by-column in
// avalue
// * The position in aindex/avalue of the index/value of the first
// nonzero in each column is stored in astart
// Note that astart[0] must be zero
// After a successful call to Highs_call, the primal and dual
// solution, and the simplex basis are returned as follows
// The vector x is colvalue
// The vector Ax is rowvalue
// The vector of dual values for the variables x is coldual
// The vector of dual values for the variables Ax is rowdual
// The basic/nonbasic status of the variables x is colbasisstatus
// The basic/nonbasic status of the variables Ax is rowbasisstatus
// The status of the solution obtained is modelstatus
// To solve maximization problems, the values in c must be negated
// The use of Highs_call is illustrated for the LP
// Min    f  = 2x_0 + 3x_1
// s.t.                x_1 <= 6
//       10 <=  x_0 + 2x_1 <= 14
//        8 <= 2x_0 +  x_1
// 0 <= x_0 <= 3; 1 <= x_1

fn main() {
    let numcol: usize = 2;
    let numrow: usize = 3;
    let nnz: usize = 5;

    // Define the column costs, lower bounds and upper bounds
    let colcost: &mut [f64] = &mut [2.0, 3.0];
    let collower: &mut [f64] = &mut [0.0, 1.0];
    let colupper: &mut [f64] = &mut [3.0, 1.0e30];
    // Define the row lower bounds and upper bounds
    let rowlower: &mut [f64] = &mut [-1.0e30, 10.0, 8.0];
    let rowupper: &mut [f64] = &mut [6.0, 14.0, 1.0e30];
    // Define the constraint matrix column-wise
    let astart: &mut [c_int] = &mut [0, 2];
    let aindex: &mut [c_int] = &mut [1, 2, 0, 1, 2];
    let avalue: &mut [f64] = &mut [1.0, 2.0, 1.0, 2.0, 1.0];

    let colvalue: &mut [f64] = &mut vec![0.; numcol];
    let coldual: &mut [f64] = &mut vec![0.; numcol];
    let rowvalue: &mut [f64] = &mut vec![0.; numrow];
    let rowdual: &mut [f64] = &mut vec![0.; numrow];

    let colbasisstatus: &mut [c_int] = &mut vec![0; numcol];
    let rowbasisstatus: &mut [c_int] = &mut vec![0; numrow];

    let modelstatus: &mut c_int = &mut 0;

    let status: c_int = unsafe {

    assert_eq!(status, 0);
    // The solution is x_0 = 2 and x_1 = 4
    assert_eq!(colvalue, &[2., 4.]);

For more examples, have a look at tests.

No runtime deps