We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Global optimization for non-convex programs via convex proximal point method.
- Authors
Zhao, Yuanyi; Xing, Wenxun
- Abstract
In this study, a convex proximal point algorithm (CPPA) is considered for solving constrained non-convex problems, and new theoretical results are proposed. It is proved that every cluster point of CPPA is a stationary point, and the initial point of CPPA is key to global optimization. Several sufficient conditions for the initial point selection are provided for CPPA to find the global minimum. Motivated by these results, numerical experiments were conducted on non-convex quadratic programming problems with convex quadratic constraints. The performance of CPPAs was compared, with the initial point randomly selected or obtained through the Lagrangian dual problem. The numerical results demonstrate that the quality of the CPPA with the computed Lagrangian dual initial point is much better than that with the random initial point, in terms of the objective function value.
- Subjects
GLOBAL optimization; NONCONVEX programming; QUADRATIC programming; CONVEX programming
- Publication
Journal of Industrial & Management Optimization, 2023, Vol 19, Issue 6, p1
- ISSN
1547-5816
- Publication type
Article
- DOI
10.3934/jimo.2022142