inst/HiGHS/docs/src/guide/basic.md

In highs: 'HiGHS' Optimization Solver

Basic features

The minimal use of HiGHS has the following three stages.

• Define a model
• Solve the model
• Extract results

Although its default actions will be sufficient for most users, HiGHS can be controlled by setting Option values.

Intro to other basic features

HiGHS data structures

There are several specialist data structures that can be used to interact with HiGHS when using C++ and highspy. These are defined in the sections on enums and classes, and are referred to below.

Enums

Enums are scalar identifier types that can take only a limited range of values.

Classes

The advantage of using the native C++ classes in HiGHS is that many fewer parameters are needed when passing data to and from HiGHS. The binding of the data members of these classes to highspy structures allows them to be used when calling HiGHS from Python, although they are not necessary for the basic use of highspy. As with the C and Fortran interfaces, there are equivalent methods that use simple scalars and vectors of data.

Defining a model

HiGHS has comprehensive tools for defining models. This can be done by either reading a model using a data file created elsewhere, or by passing model data to HiGHS. Once a model has been defined in HiGHS, it is referred to as the incumbent model.

Reading a model from a file

The simplest way to define a model in HiGHS is to read it from a file using the method readModel. HiGHS infers the file type by the extension. Supported extensions are:

• .mps: for an MPS file
• .lp: for a CPLEX LP file

HiGHS can read compressed files that end in the .gz extension, but not (yet) files that end in the .zip extension.

Building a model

The model in HiGHS can be built using a sequence of calls to add variables and constraints. This is most easily done one-by-one using the methods addCol and addRow. Alternatively, calls to addVar can be used to add variables, with calls to changeColCost used to define each objective coefficient.

Addition of multiple variables and constraints can be achieved using addCols and addRows. Alternatively, addVars can be used to add variables, with changeColsCost used to define objective coefficients. Note that defining multiple variables and constraints requires vectors of data and the specification of constraint coefficients as compressed row-wise or column-wise matrices.

Passing a model

If the entire definition of a model is known, then it can be passed to HiGHS via individual data arrays using the method passModel. In languages where HiGHS data structures can be used, an instance of the HighsLp class can be populated with data and then passed.

Solving the model

The incumbent model in HiGHS is solved by a call to the method run. By default, HiGHS minimizes the model's objective function, although this can be changed. Where possible, HiGHS will reduce the solution time by using data obtained on previous runs, or supplied by the user. More information on this process of hot starting solvers is available.

Extracting results

After solving a model, it is important to know whether it has been solved to optimality, shown to be infeasible or unbounded, or why the solver may have terminated early. This model status is given by the value returned by the method getModelStatus. This value is of type HighsModelStatus. Scalar information about a solved model is obtained using the method getInfo. The solution and (any) basis are returned by the methods getSolution and getBasis respectively. HiGHS can also be used to write the solution to a file using the method writeSolution.

Option values

The option values that control HiGHS are of type string, bool, int and double. Options are referred to by a string identical to the name of their identifier. A full specification of the options is given in the list of options. An option value is changed by passing its name and value to the method setOptionValue. The current value of an option is obtained by passing its name to the method getOptionValue.

highs documentation built on April 4, 2025, 3:23 a.m.