A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints
Keyword(s):
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.
1992 ◽
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pp. 207-226
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2017 ◽
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pp. 503-532
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2001 ◽
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pp. 208-212
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pp. 553-559
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