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Optimization of Mixed-Integer Non-Linear Programming (MINLP) supportsimportant decisions in applications such as Chemical Process Engineering. Butcurrent solvers have limited ability for deductive reasoning or the use ofdomain-specific theories, and the management of integrality constraints doesnot yet exploit automated reasoning tools such as SMT solvers. This seems tolimit both scalability and reach of such tools in practice. We thereforepresent a tool, ManyOpt, for MINLP optimization that enables experimentationwith reduction techniques which transform a MINLP problem to feasibilitychecking realized by an SMT solver. ManyOpt is similar to the SAT solverManySAT in that it runs a specified number of such reduction techniques inparallel to get the strongest result on a given MINLP problem. The tool isimplemented in layers, which we may see as features and where reductiontechniques are feature vectors. Some of these features are inspired by knownMINLP techniques whereas others are novel and specific to SMT. Our experimentalresults on standard benchmarks demonstrate the benefits of this approach. Thetool supports a variety of SMT solvers and is easily extensible with newfeatures, courtesy of its layered structure. For example, logical formulas fordeductive reasoning are easily added to constrain further the optimization of aMINLP problem of interest.

Manyopt: An Extensible Tool for Mixed, Non-Linear Optimization Through SMT Solving