Modeling with Metaconstraints and Semantic Typing of Variables
Andre Cire (acireutsc.utoronto.ca)
Abstract: Recent research in hybrid optimization shows that a combination of technologies that exploits their complementary strengths can significantly speed up computation. The use of high-level metaconstraints in the problem formulation can achieve a substantial share of these computational gains by better communicating problem structure to the solver. During the solution process, however, metaconstraints give rise to reformulations or relaxations that introduce auxiliary variables, and some of the variables in one metaconstraint's reformulation may be functionally the same as or related to variables in another metaconstraint's reformulation. These relationships must be recognized to obtain a tight overall relaxation. We propose a modeling scheme based on semantic typing that systematically addresses this problem while providing simpler, self-documenting models. It organizes the model around predicates and declares variables by associating each with a predicate through a keyword that is analogous to a database query. We present a series of examples to illustrate this idea over a wide variety of applications.
Keywords: modeling, hybrid methods, metaconstraints, semantics
Category 1: Optimization Software and Modeling Systems (Modeling Languages and Systems )
Category 2: Optimization Software and Modeling Systems (Optimization Software Design Principles )
Citation: Submitted for publication. Revised in June 2015.
Entry Submitted: 11/13/2013
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