Independent of BCL, there are Python and MATLAB interfaces. Xpress has a modeling module called BCL (Builder Component Library) that interfaces to the C, C++, Java programming languages, and to the. Uncertainty in the input data can be handled via robust optimization methods. Mosel includes distributed computing features to solve multiple scenarios of an optimization problem in parallel. Xpress includes its modelling language Xpress Mosel and the integrated development environment Xpress Workbench. Xpress provides a built-in tuner for automatic tuning of control settings. Infeasible problems can be analyzed via the IIS ( irreducible infeasible subset) method. All mixed integer programming variants are solved by a combination of the branch and bound method and the cutting-plane method. Linear and quadratic programs can be solved via the primal simplex method, the dual simplex method or the barrier interior point method. Since 2014, Xpress features the first commercial implementation of a parallel dual simplex method. Xpress was the first MIP solver to cross the billion matrix non-zero threshold by introducing 64-bit indexing in 2010. In 1992, an Xpress version for parallel computing was published, which was extended to distributed computing five years later. The first version of Xpress could only solve LPs support for MIPs was added in 1986.īeing released in 1983, Xpress was the first commercial LP and MIP solver running on PCs. Its initial authors were Bob Daniel and Robert Ashford. Xpress was originally developed by Dash Optimization, and was acquired by FICO in 2008. 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). 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. ACM Computing Surveys, 28(4):701-726, 1996.Operations Research, Mathematical optimization Strategic directions in constraint programming. Computational Optimization and Applications, 3:111-130, 1994. Solving binary cutting stock problems by column generation and branch-and-bound. ACM Computing Surveys, 28(4es):75, December 1996. Exact ground states of two-dimensional ± J ising spin glasses. A branch and cut algorithm for the resolution of large scale symmetric traveling salesman problems. The Traveling Salesman Problem and its Variations, chapter Polyhedral theory, branch and cut algorithms for the symmetric traveling salesman problem. The LEDA Platform for Combinatorial and Geometric Computing. ![]() LEDA (Library of Efficient Data Types and Algorithms).PhD thesis, Fachbereich Informatik, Universität des Saarlandes, 1998. A Unifying Logical Framework for Integer Linear Programming and Finite Domain Constraint Programming. The abacus system for branch and cut and price algorithms in integer programming and combinatorial optimization. A cutting plane algorithm for the linear ordering problem. Duxbury Press/Wadsworth Publishing, 1992. AMPL: A modeling language for Mathematical Programming. Computational Combinatorial Optimization, volume 2241 of Lecture Notes in Computer Science, chapter Branch-and-cut algorithms for combinatorial optimization and their implementation in ABACUS, pages 157-222. In Fifth Generation Computer Systems, Tokyo, 1988. The constraint logic programming language CHIP. Solution of a large scale traveling salesman problem. XPRESS 12 Reference Manual: XPRESS-MP Optimizer Subroutine Library XOSL, 2000. ![]() Gams: General algebraic modeling system, 2002. Annotated bibliographies in combinatorial optimization, chapter Branch-and-cut algorithms, pages 45-64. Branch and infer: a unifying framework for integer and finite domain constraint programming. Branch-and-price: column generation for solving huge integer programs. The precedence constrained asymmetric traveling salesman polytope. ![]() Traveling salesman-based curve reconstruction in polynomial time. In Proceedings of the 4th Annual International Conference on Computational Molecular Biology (RECOMB-00). A combinatorial approach to protein docking with flexible side-chains. Computational Optimization and Applications, 17(1):61-84, 2000. A branch & cut algorithm for the asymmetric traveling salesman problem with precedence constraints.
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