Computational Management Science

, Volume 7, Issue 4, pp 437–463 | Cite as

DrAmpl: a meta solver for optimization problem analysis

Original Paper

Abstract

Optimization problems modeled in the AMPL modeling language (Fourer et al., in AMPL: a modeling language for mathematical programming, 2002) may be examined by a set of tools found in the AMPL Solver Library (Gay, in Hooking your solver to AMPL, 1997). DrAmpl is a meta solver which, by use of the AMPL Solver Library, dissects such optimization problems, obtains statistics on their data, is able to symbolically prove or numerically disprove convexity of the functions involved and provides aid in the decision for an appropriate solver. A problem is associated with a number of relevant solvers available on the NEOS Server for Optimization (Czyzyk et al., in IEEE J Comput Sci Eng 5:68–75, 1998) by means of a relational database. We describe the need for such a tool, the design of DrAmpl and some of its consequences, and keep in mind that a similar tool could be developed for other algebraic modeling languages.

Keywords

Optimization model AMPL modeling language Directed acyclic graph Convexity assessment Structural model analysis Solver recommendation 

JEL Classification

C61 C63 

Mathematics Subject Classification (2000)

90C25 90C26 90C30 

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Department of Industrial Engineering and Management SciencesNorthwestern UniversityEvanstonUSA
  2. 2.Département de Mathématiques et Génie IndustrielGERAD and École Polytechnique de MontréalMontrealCanada

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