The Effect of Hessian Evaluations in the Global Optimization {\alpha}BB Method
Optimization and Control
2019-05-27 v1
Abstract
We consider convex underestimators that are used in the global optimization {\alpha}BB method and its variants. The method is based by augmenting the original nonconvex function by a relaxation term that is derived from an interval enclosure of the Hessian matrix. In this paper, we discuss the advantages of symbolic computation of the Hessian matrix. Symbolic computation often allows simplifications of the resulting expressions, which in turn means less conservative underestimators. We show by examples that even a small manipulation with the symbolic expressions, which can be processed automatically by computers, can have a large effect on the quality of underestimators.
Keywords
Cite
@article{arxiv.1307.2791,
title = {The Effect of Hessian Evaluations in the Global Optimization {\alpha}BB Method},
author = {Milan Hladík},
journal= {arXiv preprint arXiv:1307.2791},
year = {2019}
}
Comments
11 pages, 6 figures