Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5776038 | Journal of Computational and Applied Mathematics | 2018 | 13 Pages |
Abstract
The Primal-Dual Hybrid Gradient (PDHG) algorithm is a powerful algorithm used quite frequently in recent years for solving saddle-point optimization problems. The classical application considers convex functions, and it is well studied in literature. In this paper, we consider the convergence of an alternative formulation of the PDHG algorithm in the nonconvex case under the precompact assumption. The proofs are based on the Kurdyka-Å ojasiewic functions, that cover a wide range of problems. A simple numerical experiment illustrates the convergence properties.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Tao Sun, Roberto Barrio, Lizhi Cheng, Hao Jiang,