A validation and verification tool for global optimization solvers

Linearly constrained optimization models based on a systems description often possess multiple local optima. Therefore, there are a broad variety of problems in which the property of unique solution cannot be simply postulate or verified. The aim of global optimization (GO) is to find the best possible solution of multi-extremal problems. This paper illustrates the applicability of GO modeling techniques and solution strategies to real-world problems. In this paper we propose an enumeration approach for solving the linearly constrained optimization problem with continuous but general objective function. The method uses a parametric but unconstrained representation of the problem in terms of the vertices (and the extreme rays) of the feasible region.