Canonical Primal-Dual Method for Solving Non-convex Minimization Problems
Numerical Analysis
2013-01-01 v1 Data Structures and Algorithms
Optimization and Control
Abstract
A new primal-dual algorithm is presented for solving a class of non-convex minimization problems. This algorithm is based on canonical duality theory such that the original non-convex minimization problem is first reformulated as a convex-concave saddle point optimization problem, which is then solved by a quadratically perturbed primal-dual method. %It is proved that the popular SDP method is indeed a special case of the canonical duality theory. Numerical examples are illustrated. Comparing with the existing results, the proposed algorithm can achieve better performance.
Cite
@article{arxiv.1212.6492,
title = {Canonical Primal-Dual Method for Solving Non-convex Minimization Problems},
author = {Changzhi Wu and Chaojie Li and David Yang Gao},
journal= {arXiv preprint arXiv:1212.6492},
year = {2013}
}
Comments
21 pages, 6 figures and 4 tables