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2013 A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints
Xiaoling Fu
Abstr. Appl. Anal. 2013(SI54): 1-13 (2013). DOI: 10.1155/2013/492305

Abstract

We present an efficient method for solving linearly constrained convex programming. Our algorithmic framework employs an implementable proximal step by a slight relaxation to the subproblem of proximal point algorithm (PPA). In particular, the stepsize choice condition of our algorithm is weaker than some elegant PPA-type methods. This condition is flexible and effective. Self-adaptive strategies are proposed to improve the convergence in practice. We theoretically show under mild conditions that our method converges in a global sense. Finally, we discuss applications and perform numerical experiments which confirm the efficiency of the proposed method. Comparisons of our method with some state-of-the-art algorithms are also provided.

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Xiaoling Fu. "A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints." Abstr. Appl. Anal. 2013 (SI54) 1 - 13, 2013. https://doi.org/10.1155/2013/492305

Information

Published: 2013
First available in Project Euclid: 26 February 2014

zbMATH: 1291.90170
MathSciNet: MR3108415
Digital Object Identifier: 10.1155/2013/492305

Rights: Copyright © 2013 Hindawi

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Vol.2013 • No. SI54 • 2013
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