HiGHS

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HiGHS is a high performance serial and parallel solver for large-scale sparse linear programming (LP) models developed in C++11, with interfaces to C, C#, FORTRAN, Julia and Python.

HiGHS is freely available under the MIT licence, and is downloaded from Github. Installing HiGHS from source code requires CMake minimum version 3.0.0, but no other third-party utilities. HiGHS can be used as a standalone executable on Windows and Linux. There is a C++11 library which can be used within a C++ project or, via one of the interfaces, to a project written in other languages.

Information on how to install and use HiGHS is given in the guide below, and full documentation can be built using doxygen. Your comments or specific questions on HiGHS would be greatly appreciated, so please send an email to highsopt@gmail.com to get in touch with the team.

Guide

This guide sets out what HiGHS can be used for, and how to build and use it. HiGHS can be run from a command line or by calling it from within another application via a library.

Specification

HiGHS can load LP models from data files or via data provided by another application. It can solve LP models using the dual revised simplex method. The solution can be written to a file or retrieved directly for use within an application. Within an application, HiGHS can be used to modify the current model and re-solve it efficiently.

Building HiGHS

HiGHS uses CMake as a build system. The simplest setup is to create a build folder (within the folder into which HiGHS has been downloaded) and then build HiGHS within it. The name of the build folder is arbitrary but, assuming it is HiGHS/build, the full sequence of commands required is as follows

  mkdir build
  cd build
  cmake ..
  make

This creates the executable build/bin/highs. To perform a quick test to see whether the compilation was successful, run ctest from within the build folder.

Note that HiGHS requires (at least) version 4.9 of the GNU gcc/g++ compiler.

Running HiGHS from the command line

In the following discussion, the name of the executable file created in build/bin when building HiGHS is assumed to be highs. HiGHS can read plain text MPS files and LP files (but not compressed files), and the following command solves the model in ml.mps

  highs ml.mps

HiGHS is controlled by means of options.

Running HiGHS from within an application

To run HiGHS from an application, the key requirements are to load a model, solve it and extract the results. Many users will want to get and set option values to control the way HiGHS runs. It is also possible to extract model data and modify a model. HiGHS can perform other operations for specialist applications. HiGHS is linked into another application via a library.

Although HiGHS is written in C++, interfaces exist for C, C#, FORTRAN, Julia and Python. To within the limits of the language, they offer the same functionality that is available from C++, with method names that are either identical or distinguished by a consistent name extension. The discussion below refers to the C++ methods, followed by an account of the language-specific characteristics of the interfaces.

Example programs calling HiGHS from C, C#, FORTRAN, Julia and Python are in HiGHS/examples.

Before the C++ methods can be used, an instance of the Highs class must be created. Some methods are used to return data, and the others return an indication of success. In some cases this is a value of HighsStatus, and in others it is simply a boolean.

For full information on the detailed use of the HiGHS methods discussed below, consult the documentation built with doxygen. This is created by calling ./doxygen in HiGHS/docs and viewed by loading HiGHS/docs/index.html into any web browser.

Loading a model

The simplest way to use HiGHS to solve a model is to load a model from a file using the method readModel. Different file formats are recognised from the filename extension. HiGHS can read plain text MPS files and LP, but not compressed files. Alternatively, in C++, data generated by an application can be passed to HiGHS via an instance of the HighsLp class populated by the user and passed using the method passModel. In languages where such structures cannot be used, data constituting an LP model can be passed via individual parameters.

Solving a model

HiGHS is used to solve a model by calling the method run. By default, HiGHS minimizes the model's objective function.

Extracting the results

After solving a model, its status is the value returned by the method getModelStatus. This value is of type HighsModelStatus, and may be interpreted via the names in the corresponding enum. The objective function value and iteration count are obtained using the methods getObjectiveValue and getIterationCount. The solution and basis are returned by the methods getSolution and getBasis respectively. Note that these are const references to internal data. HiGHS can also be used to write the solution to a file using the method writeSolution, with the output going to stdout if the filename is blank.

Extracting model data

The numbers of column, rows and nonzeros in the model are returned by the methods getNumCols, getNumRows and getNumEntries respectively. Data for columns and rows from the model may be extracted using the methods getCols and getRows, and specific matrix coefficients obtained using the method getCoeff.

Modifying a model

The most immediate model modification is to change the sense of the objective. By default, HiGHS minimizes the model's objective function. The objective sense can be set to minimize (maximize) by passing the value 1 (-1) to the method changeObjectiveSense. The cost coefficient or bounds of a column are changed by passing its index and new value(s) to the methods changeColCost, changeColBounds. The corresponding method for a row is changeRowBounds.

For the convenience of application developers, data for multiple columns and rows can be changed in three different ways in HiGHS. This is introduced in the case of column costs. The columns can be defined by the first and last indices of the interval of columns whose costs will be changed, together with the corresponding values. When costs for a non-contiguous set of columns are changed, they may be specified as a set of indices (which need not be ordered), the number of entries in the set and the corresponding values. Alternatively, the columns to be changed (not changed) may be specified by setting values of +1 (0) in an integer mask of dimension equal to the number of columns, together with a full-length vector of values. In all three cases, the method used is called changeColsCosts. The bounds of multiple columns or rows are changed using the methods changeColsBounds or changeRowsBounds respectively.

An individual matrix coefficient is changed by passing its row index, column index and new value to changeCoeff.

To add a column or row to the model, pass the necessary data to the method addCol or addRow respectively. Multiple columns and rows can be added using the methods addCols or addRows.

Columns or rows can be deleted from the model using the methods deleteCols or deleteRows. As above, the columns or rows to be deleted may be specified as a contiguous interval, a set or via a mask. In the case of the latter, the new indices of any remaining columns or rows are returned in place of the entries of 0.

Other operations

To run HiGHS from a user-defined solution or basis, this is passed to HiGHS using the methods setSolution or setBasis.

HiGHS has a suite of methods for operations with the invertible representation of the current basis matrix $B$. To use these requires knowledge of the corresponding (ordered) basic variables. This is obtained using the method getBasicVariables, with non-negative values being columns and negative values corresponding to row indices plus one [so -1 indicates row 0]. Methods getBasisInverseRow and getBasisInverseCol yield a specific row or column of $B-1$. Methods getBasisSolve and getBasisTransposeSolve yield the solution of $Bx=b$ and $Bx=b$ respectively. Finally, the methods getReducedRow and getReducedColumn yield a specific row or column of $B-1A$. In all cases, HiGHS can return the number and indices of the nonzeros in the result.

Library

HiGHS may be used to create a shared library. Running

  make install

from the build folder attempts to install the executable in /usr/local/bin, the library in /usr/local/lib, and the header files in /usr/local/include. For a custom installation based in install_folder run

  cmake .. -DCMAKE_INSTALL_PREFIX=install_folder

and then

  make install

Using HiGHS in a CMake project

To use the library from a CMake project use find_package(HiGHS) and add the correct path to HIGHS_DIR.

Compiling and linking without CMake

An executable defined in the file use_highs.cpp is linked with the HiGHS library as follows. Assuming that the custom installation is based in install_folder, 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

Language interfaces

C# interface

From an application written in C#, HiGHS is run by creating a HighsLpSolver instance thus

  HighsLpSolver solver = new HighsLpSolver;

An LP model may then be read in from a file solver.readModel, or communicated to HiGHS by passing its component arrays of data to HighsModel. This returns an object that can then be passed to the HighsLpSolver instance via solver.passLp. The LP is solved using solver.run, and data extracted as described above for the C++ interface.

Background

HiGHS is based on the high performance dual revised simplex implementation (HSOL) and its parallel variant (PAMI) developed by Qi Huangfu. Features such as presolve, crash and advanced basis start have been added by Julian Hall and Ivet Galabova. Other features and the interfaces have been written by Michael Feldmeier.

Citation

In the absence of a release paper, academic users of HiGHS are kindly requested to 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