FICO Xpress

The FICO Xpress optimizer is a commercial optimization solver for linear programming (LP), mixed integer linear programming (MILP), convex quadratic programming (QP), convex quadratically constrained quadratic programming (QCQP), second-order cone programming (SOCP) and their mixed integer counterparts.[2] Xpress includes a general purpose non-linear solver, Xpress NonLinear, including a successive linear programming algorithm (SLP, first-order method), and Artelys Knitro (second-order methods).

FICO Xpress
Developer(s)FICO
Initial release1983 (1983)
Stable release
9.2[1]
PlatformCross-platform
TypeOperations Research, Mathematical optimization
LicenseProprietary
Websitewww.fico.com/en/products/fico-xpress-optimization

Xpress was originally developed by Dash Optimization, and was acquired by FICO in 2008.[3] Its initial authors were Bob Daniel and Robert Ashford. The first version of Xpress could only solve LPs; support for MIPs was added in 1986. Being released in 1983, Xpress was the first commercial LP and MIP solver running on PCs.[4] In 1992, an Xpress version for parallel computing was published, which was extended to distributed computing five years later.[5] Xpress was the first MIP solver to cross the billion matrix non-zero threshold by introducing 64-bit indexing in 2010.[6] Since 2014, Xpress features the first commercial implementation of a parallel dual simplex method.[2]

Technology

Linear and quadratic programs can be solved via the primal simplex method, the dual simplex method or the barrier interior point method. All mixed integer programming variants are solved by a combination of the branch and bound method and the cutting-plane method. Infeasible problems can be analyzed via the IIS (irreducible infeasible subset) method. Xpress provides a built-in tuner for automatic tuning of control settings. [1] Xpress includes its modelling language Xpress Mosel[7] and the integrated development environment Xpress Workbench.[8] Mosel includes distributed computing features to solve multiple scenarios of an optimization problem in parallel. Uncertainty in the input data can be handled via robust optimization methods.[9]

Xpress has a modeling module called BCL (Builder Component Library) that interfaces to the C, C++, Java programming languages, and to the .NET Framework.[10] Independent of BCL, there are Python and MATLAB interfaces. Next to Mosel, Xpress connects to other standard modeling languages, such as AIMMS, AMPL, and GAMS.

The FICO Xpress Executor[11] executes and deploys Mosel models, using SOAP or REST interfaces. It can be used from external applications or from the FICO Decision Management Platform.

References
  1. "FICO Xpress Optimization".
  2. Berthold, T.; Farmer, J.; Heinz, S.; Perregaard, M. (15 Jun 2017). "Parallelization of the FICO Xpress-Optimizer". Optimization Methods and Software. 33 (3): 518–529. doi:10.1080/10556788.2017.1333612.
  3. "Dash Optimization acquired by FICO" Jan 22, 2008.
  4. Ashford, R. (Feb 2007). "Mixed integer programming: A historical perspective with Xpress-MP". Annals of Operations Research. 149 (1): 5–17. doi:10.1007/s10479-006-0092-x.
  5. Laundy, R. (1999). "Implementation of Parallel Branch-and-bound Algorithms in XPRESS-MP". pp. 25–41. doi:10.1057/9780230372924_2. ISBN 9780230372924. {{cite book}}: |journal= ignored (help); Missing or empty |title= (help)
  6. O. Bastert (2011). FICO Xpress Optimization Suite (PDF) (Report). Retrieved Jan 23, 2019.
  7. Guéret, Christelle; Prins, Christian; Sevaux, Marc (2002). Applications of Optimization with Xpress-MP. Dash Optimization Limited. ISBN 9780954350307.
  8. "FICO Xpress Workbench". Nov 12, 2017.
  9. P. Belotti (2014). Robust Optimization with Xpress (PDF) (Report). Retrieved Oct 28, 2018.
  10. "BCL Reference Manual" Nov 13, 2018.
  11. "FICO Xpress Executor" Nov 13, 2018.
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