Convergence Analysis on an Accelerated Proximal Point Algorithm for Linearly Constrained Optimization Problems. (10th November 2020)

• Record Type: Journal Article
• Authors: Lu, Sha; Wei, Zengxin
• Other Names: Zhou Weijun Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-11-10
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/8873507
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Proximal point algorithm is a type of method widely used in solving optimization problems and some practical problems such as machine learning in recent years. In this paper, a framework of accelerated proximal point algorithm is presented for convex minimization with linear constraints. The algorithm can be seen as an extension to Gu ¨ ler's methods for unconstrained optimization and linear programming problems. We prove that the sequence generated by the algorithm converges to a KKT solution of the original problem under appropriate conditions with the convergence rate of O 1 / k 2 .