Initializing help system before first use

Decide which Xpress products to evaluate

Topics covered in this section:

In order to make this decision you must consider the type of problem that you wish to solve, the tools that you wish to use for model development, the tools that you wish to use for model deployment, and the platform you intend to use.

Type of problem

Xpress has solver engines for different types of problems:

Type of problem Solving engine
Linear Programming (LP) Xpress Optimizer
Mixed Integer Programming (MIP)
Quadratic Programming (QP)
Mixed Integer Quadratic Programming (MIQP)
Quadratically Constrained Quadratic Programming (QCQP/MIQCQP)
Second Order Cone Programming (SOCP/MISOCP)
Non-Linear Programming (NLP) Xpress NonLinear, Xpress Global
Mixed Integer Non-Linear Programming (MINLP)
Constraint Programming (CP) Xpress Kalis

Further information

See Xpress documentation (in the docs subdirectory of your Xpress installation or at https://www.fico.com/fico-xpress-optimization/docs/latest):

Tools for model development and specification

Xpress provides the following tools for model development and specification:

  • Xpress Mosel/Xpress Workbench: Mosel is an advanced modeling and programming language. Xpress Workbench is the visual environment to develop Mosel models and Insight apps. As an alternative to Workbench, FICO also provides a VSCode extension for working with Mosel.
  • Xpress Solver Python API/Xpress Workbench: The Xpress Workbench environment also supports the Xpress Python APIs for the development of optimization models and their deployment as Insight apps.
  • Xpress Solver libraries: Optimizer functions and procedures provided for low-level integration with applications developed in supported programming languages; unified matrix-based and object-oriented modeling APIs for supported object-oriented programming languages.

Also, a model may be specified by a matrix in MPS or LP format.

Tools for model deployment

Model deployment strongly depends on the tool used to specify the model. Deployment is achieved through the use of Xpress libraries. Models developed with Mosel or Python can be embedded into distributed, multi-user applications using Xpress Insight. Mosel models can also be deployed as distributed or Cloud computing applications controled via XPRD (see the Advanced Evaluators Guide for further detail). Furthermore, Mosel and Python models can be embedded into Python notebooks that can be run online without requiring any local installation of Xpress.

Model specified in  Deployed using
Mosel Xpress Insight
Mosel libraries (C/C++, Java, VBA, .NET, Python)
XPRD (C/C++, Java)
Optimizer libraries Xpress Insight (Python API only)
Optimizer libraries (C, C++, Java, .NET, Python, R)
Kalis libraries Kalis libraries (C++, Java, Python)

Further information

See Xpress documentation (in the docs subdirectory of your Xpress installation or at http://www.fico.com/fico-xpress-optimization/docs/latest):

Platform

Xpress Workbench is available on Microsoft Windows platforms, via Docker on macOS, and on the FICO Analytic Cloud. Xpress Kalis is available on Microsoft Windows and Linux. All other products are available on every supported platform.


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