作者: Milan Hladík
DOI: 10.1007/S10898-014-0161-7
关键词: Scaling 、 State (functional analysis) 、 Hessian matrix 、 Heuristics 、 Global optimization 、 Alpha (programming language) 、 Mathematical optimization 、 Mathematics 、 Eigenvalues and eigenvectors 、 Global optimization problem 、 Management Science and Operations Research 、 Control and Optimization 、 Applied mathematics 、 Computer Science Applications
摘要: In this paper, we revisit the $$\alpha $$ ? BB method for solving global optimization problems. We investigate optimality of scaling vector used in Gerschgorin's inclusion theorem to calculate bounds on eigenvalues Hessian matrix. propose two heuristics compute a good $$d$$ d , and state three necessary conditions an optimal vector. Since vectors calculated by presented methods satisfy all conditions, they serve as cheap but efficient solutions. A small numerical study shows that are practically always optimal.