DrAmpl: a meta solver for optimization problem analysis

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.