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

Xiaoling Fu

Abstract and Applied Analysis, 2013, vol. 2013, issue 1

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.

Date: 2013
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https://doi.org/10.1155/2013/492305

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnlaaa:v:2013:y:2013:i:1:n:492305

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