Computational Experience with a New Class of Convex Underestimators: Box-constrained NLP Problems |
| |
Authors: | Ioannis G Akrotirianakis Christodoulos A Floudas |
| |
Institution: | (1) Department of Chemical Engineering, Princeton University, Princeton, NJ 08544, USA |
| |
Abstract: | In Akrotirianakis and Floudas (2004) we presented the theoretical foundations of a new class of convex underestimators for C
2 nonconvex functions. In this paper, we present computational experience with those underestimators incorporated within a Branch-and-Bound algorithm for box-conatrained problems. The algorithm can be used to solve global optimization problems that involve C
2 functions. We discuss several ways of incorporating the convex underestimators within a Branch-and-Bound framework. The resulting Branch-and-Bound algorithm is then used to solve a number of difficult box-constrained global optimization problems. A hybrid algorithm is also introduced, which incorporates a stochastic algorithm, the Random-Linkage method, for the solution of the nonconvex underestimating subproblems, arising within a Branch-and-Bound framework. The resulting algorithm also solves efficiently the same set of test problems. |
| |
Keywords: | Branch-and-bound convex underestimators global optimization |
本文献已被 SpringerLink 等数据库收录! |
|