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A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints

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  • Xiaoling Fu

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, 2013. "A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-13, September.
    • Handle: RePEc:hin:jnlaaa:492305
    DOI: 10.1155/2013/492305
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