Algorithms for discrete nonlinear optimization in FICO Xpress

The FICO Xpress-Optimizer is a commercial optimization solver for linear programming (LP), mixed integer linear programming (MIP), convex quadratic programming (QP), convex quadratically constrained quadratic programming (QCQP), second-order cone programming (SOCP) and their mixed-integer counterpar...

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Bibliographic Details
Published in:Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop pp. 1 - 5
Main Authors: Belotti, Pietro, Berthold, Timo, Neves, Kelligton
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2016
Subjects:
Linear programming
Mathematical model
Optimization
Programming
Search problems
Signal processing
Signal processing algorithms
ISSN:2151-870X
Online Access:Get full text
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Description
Summary:The FICO Xpress-Optimizer is a commercial optimization solver for linear programming (LP), mixed integer linear programming (MIP), convex quadratic programming (QP), convex quadratically constrained quadratic programming (QCQP), second-order cone programming (SOCP) and their mixed-integer counterparts. Xpress also includes a general purpose non-linear solver, Xpress-NonLinear, which features a successive linear programming algorithm (SLP, first-order method), interior point methods and Artelys Knitro (second-order methods). This work explores algorithms for mixed-integer nonlinear programming problems (MINLPs), which are NP-hard in general, then it presents applications in signal processing and capitalizes advances in solving these problems with Xpress and its comprehensive suite of high-performance nonlinear solvers. Computational results show that signal processing nonlinear problems can be solved quickly and accurately, taking advantage of the algebraic modeling and procedural programming language, Xpress-Mosel, that allows to interact with the Xpress solver engines in a easy-to-learn way, and its unified modeling interface for all solvers, from linear to general nonlinear solvers.
ISSN:2151-870X
DOI:10.1109/SAM.2016.7569658