Scimitar: Functional Programs as Optimization Problems

Abstract

Mixed integer linear programming is a powerful and widely used approach to solving optimization problems, but its expressiveness is limited. In this paper we introduce the optimization-aided language Scimitar, which encodes optimization problems using an expressive functional language, with a compiler that targets a mixed integer linear program solver. Scimitar provides easy access to encoding techniques that normally require expert knowledge, enabling solve-time conditional constraints, inlining, loop unrolling, and many other high-level language constructs. We give operational semantics for Scimitar and constraint encodings of various features. To demonstrate Scimitar, we present a number of examples and benchmarks including classic optimization domains and more complex problems. Our results indicate that Scimitar's use of a dedicated MILP solver is effective for expressively modeling optimization problems embedded within functional programs.