Detection and Transformation of Second-Order Cone Programming Problems in a General-Purpose Algebraic Modeling Language

Diverse forms of nonlinear optimization problems can be recast to the special form of second-order cone problems (SOCPs), permitting a wider variety of highly effective solvers to be applied. Popular solvers assume, however, that the necessary transformations to required canonical forms have already been identified and carried out. We describe a general approach to the … Read more

Extending Algebraic Modelling Languages for Stochastic Programming

Algebraic modelling languages have gained wide acceptance and use in Mathematical Programming by researchers and practitioners. At a basic level, stochastic programming models can be defined using these languages by constructing their deterministic equivalent. Unfortunately, this leads to very large model data instances. We propose a direct approach in which the random values of the … Read more

StAMPL: A Filtration-Oriented Modeling Tool for Stochastic Programming

Every multistage stochastic programming problem with recourse (MSPR) contains a filtration process. In this research, we created a notation that makes the filtration process the central syntactic construction of the MSPR. As a result, we achieve lower redundancy and higher modularity than is possible with the mathematical notation commonly associated with stochastic programming. To experiment … Read more

Kestrel: An Interface from Optimization Modeling Systems to the NEOS Server

The NEOS Server provides access to a variety of optimization resources via the Internet. The new Kestrel interface to the Server enables local modeling environments to request NEOS optimization services and retrieve the results for local visualization and analysis, so that users have the same convenient access to remote NEOS solvers as to those installed … Read more

OSiL: An Instance Language for Optimization

Distributed computing technologies such as Web Services are growing rapidly in importance in today’s computing environment. In the area of mathematical optimization, it is becoming increasingly common to separate modeling languages from optimization solvers. In fact, the modeling language software, solver software, and data used to generate a model instance might reside on different machines … Read more

LPFML: A W3C XML Schema for Linear and Integer Programming

There are numerous algebraic modeling languages for generating linear programs and numerous solvers for computing solutions to linear programs. This proliferation of modeling languages and solvers is frustrating to modelers who find that only certain languages connect to certain solvers. One way to encourage modeler-solver compatibility is to use a standard representation of a problem … Read more

Numerical Issues and Influences in the Design of Algebraic Modeling Languages for Optimization

This paper draws from our experience in developing the AMPL modeling language, to show where numerical issues have been crucial to modeling language design and where modeling language advances have strongly influenced the design of solvers. Citation Proceedings of the 20th Biennial Conference on Numerical Analysis, Dundee, Scotland, D.F. Griffiths and G.A. Watson, eds., University … Read more

The NEOS Server for Optimization: Version 4 and Beyond

We describe developments associated with Version 4 of the NEOS Server and note that these developments have led to an exponential growth in the number of job submissions. We also provide an overview of some of the research and educational uses for the NEOS Server and discuss future research challenges. Citation Preprint ANL/MCS-P947-0202, Argonne National … Read more

Extending an Algebraic Modeling Language to Support Constraint Programming

We describe extensions to algebraic modeling languages and their solver interfaces that will be needed to take advantage of constraint programming solvers, particularly in the area of combinatorial optimization. Citation Technical Report, Department of Industrial Engineering and Management Sciences, Northwestern University (2001); based on a shorter version that appeared in the Proceedings of the Third … Read more

OR Counterparts to AI Planning

The term Planning is not used in Operations Research in the sense that is most common in Artificial Intelligence. AI Planning does have many features in common with OR scheduling, sequencing, routing, and assignment problems, however. Current approaches to solving such problems can be broadly classified into four areas: Combinatorial Optimization, Integer Programming, Constraint Programming, … Read more