Learning To Optimize Constrained Problems

Abstract

This paper contains early work on how GOSAM, a learning based unconstrained optimization technique that we had proposed in previous work, can be extended to the constrained optimization domain. The algorithm, termed as a global optimizer using support vector regression based adaptive multistart (GOSAM), yielded highly encouraging results for unconstrained benchmark optimization problems