HiGHS is a high performance serial and parallel solver for large scale sparse linear programming (LP) problems of the form
Minimize c^Tx subject to L <= Ax <= U; l <= x <= u
and mixed integer programming (MIP) problems of the same form, for whch some of the variables must take integer values. It is mainly written in C++ with OpenMP directives, 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 is based on the dual revised simplex method implemented in HSOL, which was originally written by Qi Huangfu. Features such as presolve, crash and advanced basis start have been added by Julian Hall, Ivet Galabova. Other features, and interfaces to C, C#, FORTRAN, Julia and Python, have been written by Michael Feldmeier. The MIP solver has been written by Leona Gottwald.
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 [email protected].
If you use HiGHS in an academic context, please acknowledge this and cite the following article. P arallelizing 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
The performance of HiGHS relative to some commercial and open-source simplex solvers may be assessed via the Mittelmann benchmarks on http://plato.asu.edu/ftp/lpsimp.html
The rest of this file gives brief documentation for HiGHS. Comprehensive documentation is available via https://www.highs.dev.
HiGHS uses CMake as build system. First setup a build folder and call CMake as follows
mkdir build cd build cmake ..
Then compile the code using
This installs the executable
The minimum CMake version required is 3.15.
To perform a quick test whether the compilation was successful, run
In the following discussion, the name of the executable file generated is
assumed to be
HiGHS can read plain text MPS files and LP files and the following command
solves the model in
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"/"mip" are alternatives. --parallel arg Parallel solve: "choose" by default - "on"/"off" are alternatives. --time_limit arg Run time limit (double). --options_file arg File containing HiGHS options.
-h, --help Print help.
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 [email protected].
Parallel dual simplex is available in HiGHS under Linux, but not on Windows or MacOS due to issues relating to OpenMP. This situation should improve when parallelism in HiGHS is handled via the native C++ instructions. However, performance gain with the simplex solver is unlikely to be significant. At best, speed-up is limited to the number of memory channels, rather than the number of cores.
If OpenMP is found by CMake, the parallel code may be used. The number of threads used at run
time is the value of the environment variable
OMP_NUM_THREADS. For example,
to use HiGHS with eight threads to solve
export OMP_NUM_THREADS=8 highs --parallel ml.mps
OMP_NUM_THREADS is not set, either because it has not been set or due to
executing the command
then all available threads will be used.
If run with
OMP_NUM_THREADS=1, HiGHS is serial. The
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
When compiled with the parallel option and
OMP_NUM_THREADS>1 or unset, HiGHS
will use multiple threads. If
OMP_NUM_THREADS is unset, HiGHS will try to use
all available threads, so performance may be very slow. Although the best value
will be problem and architecture dependent,
OMP_NUM_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
from the build folder installs the library in
lib/, as well as all header files in
include/. For a custom
cmake -DCMAKE_INSTALL_PREFIX=install_folder ..
To use the library from a CMake project use
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
Set custom options with
-D<option>=<value> during the configuration step (
GAMS_ROOT: path to GAMS system: enables building of GAMS interface
If build with GAMS interface, then HiGHS can be made available as solver in GAMS by adding an entry for HiGHS to the file gmscmpun.txt in the GAMS system folder (gmscmpnt.txt on Windows):
HIGHS 11 5 0001020304 1 0 2 LP RMIP gmsgenus.run gmsgenux.out /path/to/libhighs.so his 1 1
highscrate. The rust linear programming modeler good_lp supports HiGHS.
OSI_ROOT: path to COIN-OR/Osi build/install folder (OSI_ROOT/lib/pkg-config/osi.pc should exist)