HiGHS is a high performance serial and parallel solver for large scale sparse linear optimization problems of the form
Minimize (1/2) x^TQx + c^Tx subject to L <= Ax <= U; l <= x <= u
where Q must be positive semi-definite and, if Q is zero, there may be a requirement that some of the variables take integer values. Thus HiGHS can solve linear programming (LP) problems, convex quadratic programming (QP) problems, and mixed integer programming (MIP) problems. It is mainly written in C++, but also has some C. It has been developed and tested on various Linux, MacOS and Windows installations using both the GNU (g++) and Intel (icc) C++ compilers. Note that HiGHS requires (at least) version 4.9 of the GNU compiler. It has no third-party dependencies.
HiGHS has primal and dual revised simplex solvers, originally written by Qi Huangfu and further developed by Julian Hall. It also has an interior point solver for LP written by Lukas Schork, an active set solver for QP written by Michael Feldmeier, and a MIP solver written by Leona Gottwald. Other features have been added by Julian Hall and Ivet Galabova, who manages the software engineering of HiGHS and interfaces to C, C#, FORTRAN, Julia and Python.
Although HiGHS is freely available under the MIT license, we would be pleased to learn about users' experience and give advice via email sent to highsopt@gmail.com.
If you use HiGHS in an academic context, please acknowledge this and cite the following article. Parallelizing the dual revised simplex method Q. Huangfu and J. A. J. Hall Mathematical Programming Computation, 10 (1), 119-142, 2018. DOI: 10.1007/s12532-017-0130-5
http://www.maths.ed.ac.uk/hall/HuHa13/
The project has an entry on Wikipedia: https://en.wikipedia.org/wiki/HiGHS_optimization_solver
The rest of this file gives brief documentation for HiGHS. Comprehensive documentation is available via https://www.highs.dev.
Precompiled static executables are available for a variety of platforms at: https://github.com/JuliaBinaryWrappers/HiGHSstatic_jll.jl/releases
These binaries are provided by the Julia community and are not officially supported by the HiGHS development team. If you have trouble using these libraries, please open a GitHub issue and tag @odow in your question.
Installation instructions
To install, download the appropriate file and extract the executable located at /bin/highs.
- For Windows users: if in doubt, choose the file ending in
x86_64-w64-mingw32.tar.gz - For M1 macOS users: choose the file ending in
aarch64-apple-darwin.tar.gz - For Intel macOS users: choose the file ending in
x86_64-apple-darwin.tar.gz
Shared libaries
For advanced users, precompiled executables using shared libraries are available for a variety of platforms at: https://github.com/JuliaBinaryWrappers/HiGHS_jll.jl/releases.
Similar download instructions apply.
- These files link against
libstdc++. If you do not have one installed, download the platform-specific libraries from: https://github.com/JuliaBinaryWrappers/CompilerSupportLibraries_jll.jl/releases/tag/CompilerSupportLibraries-v0.5.1%2B0 and copy all the libraries into the same folder as thehighsexecutable. - Unless using the FORTRAN interface, any of versions libgfortran3-libgfortran5 should work.
If in doubt, Windows users should choose the
x86_64-w64-mingw32-libgfortran5.tar.gz.
HiGHS uses CMake as build system. First setup a build folder and call CMake as follows
mkdir build
cd build
cmake -DFAST_BUILD=ON ..
Then compile the code using
cmake --build .
This installs the executable bin/highs.
The minimum CMake version required is 3.15.
To perform a quick test whether the compilation was successful, run
ctest
In the following discussion, the name of the executable file generated is
assumed to be highs.
HiGHS can read plain text MPS files and LP files and the following command
solves the model in ml.mps
highs ml.mps
Usage: highs [OPTION...] [file]
--model_file arg File of model to solve.
--presolve arg Presolve: "choose" by default - "on"/"off" are alternatives.
--solver arg Solver: "choose" by default - "simplex"/"ipm" are alternatives.
--parallel arg Parallel solve: "choose" by default - "on"/"off" are alternatives.
--run_crossover arg Run crossover after IPM: "on" by default - "choose"/"off" are alternatives.
--time_limit arg Run time limit (seconds - double).
--options_file arg File containing HiGHS options.
--solution_file arg File for writing out model solution.
--write_model_file arg File for writing out model.
--random_seed arg Seed to initialize random number generation.
--ranging arg Report cost, bound, RHS and basic solution ranging in any solution file: "off" by default - "on" is alternatives.
--read_solution_file File of solution to be read
--version Print version number
-h, --help Print help.
Note:
- If the file defines a quadratic term in the objective (so the problem is a QP or MIQP) and "simplex" or "ipm" is selected for the solver option, then the quadratic term will be ignored.
- If the file constrains some variables to take integer values and defines a quadratic term in the objective, then the problem is MIQP and cannot be solved by HiGHS
There are HiGHS interfaces for C, C#, FORTRAN, and Python in HiGHS/src/interfaces, with example driver files in HiGHS/examples. Documentation is availble via https://www.highs.dev/, and we are happy to give a reasonable level of support via email sent to highsopt@gmail.com.
Parallel computation within HiGHS is limited to the dual simplex solver. However, performance gain is unlikely to be significant at present. For the simplex solver, at best, speed-up is limited to the number of memory channels, rather than the number of cores.
HiGHS will identify the number of available threads at run time, and restrict their use to the value of the HiGHS option threads.
If run with threads=1, HiGHS is serial. The --parallel run-time
option will cause the HiGHS parallel dual simplex solver to run in serial. Although this
could lead to better performance on some problems, performance will typically be
diminished.
If multiple threads are available, and run with threads>1, HiGHS will use multiple threads.
Although the best value will be problem and architecture dependent, for the simplex solver threads=8 is typically a
good choice.
Although HiGHS is slower when run in parallel than in serial for some problems, it is typically faster in parallel.
HiGHS is compiled in a shared library. Running
make install
from the build folder installs the library in lib/, as well as all header files in include/. For a custom
installation in install_folder run
cmake -DCMAKE_INSTALL_PREFIX=install_folder ..
and then
make install
To use the library from a CMake project use
find_package(HiGHS)
and add the correct path to HIGHS_DIR.
An executable defined in the file use_highs.cpp (for example) is linked with the HiGHS library as follows. After running the code above, compile and run with
g++ -o use_highs use_highs.cpp -I install_folder/include/ -L install_folder/lib/ -lhighs
LD_LIBRARY_PATH=install_folder/lib/ ./use_highs
- A Julia interface is available at https://github.com/jump-dev/HiGHS.jl.
- HiGHS can be used from rust through the
highscrate. The rust linear programming modeler good_lp supports HiGHS.
- An R interface is available through the
highsR package.
HiGHS can be used from javascript directly inside a web browser thanks to highs-js. See the demo and the npm package.
Alternatively, HiGHS can directly be compiled into a single HTML file and used
in a browser. This requires emscripten to be installed from their website
(unfortunately, e.g. sudo apt install emscripten in Ubuntu Linux is broken):
https://emscripten.org/docs/getting_started/downloads.html
Then, run
sh build_webdemo.sh
This will create the file build_webdemo/bin/highs.html. For fast edit
iterations run
find src app | entr -rs 'make -C build_webdemo highs; echo'
This will rebuild highs.html every time a source file is modified (e.g.
from Visual Studio Code).
There are two ways to build the Python interface to HiGHS.
From PyPi
HiGHS has been added to PyPi, so should be installable using the command
pip install highspy
The installation can be tested using the example minimal.py, yielding the output
Running HiGHS 1.2.2 [date: 2022-09-04, git hash: 8701dbf19]
Copyright (c) 2022 ERGO-Code under MIT licence terms
Presolving model
2 rows, 2 cols, 4 nonzeros
0 rows, 0 cols, 0 nonzeros
0 rows, 0 cols, 0 nonzeros
Presolve : Reductions: rows 0(-2); columns 0(-2); elements 0(-4) - Reduced to empty
Solving the original LP from the solution after postsolve
Model status : Optimal
Objective value : 1.0000000000e+00
HiGHS run time : 0.00
or the more didactic call_highs_from_python.py.
Directly
In order to build the Python interface, build and install the HiGHS
library as described above, ensure the shared library is in the
LD_LIBRARY_PATH environment variable, and then run
pip install ./
from src/interfaces/highspy (there should be a setup.py file there).
You may also require
pip install pybind11pip install pyomo
The Python interface can then be tested as above