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A proximal multiplier method for separable convex minimization

Orlando Sarmiento (osarmiento***at***cos.ufrj.br)
Erik Papa Quiroz (erik***at***cos.ufrj.br)
Paulo Oliveira (poliveir***at***cos.ufrj.br)

Abstract: In this paper, we propose an inexact proximal multiplier method using proximal distances for solving convex minimization problems with a separable structure. The proposed method unified the work of Chen and Teboulle (PCPM method), Kyono and Fukushima (NPCPMM) and Auslender and Teboulle (EPDM) and extends the convergence properties for a class of phi-divergence distances. We prove, under standard assumptions, that the iterations generated by the method are well defined and the sequence converges to an optimal solution of the problem.

Keywords: Proximal multiplier methods; separable convex problems; proximal distances; convex functions.

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Category 3: Nonlinear Optimization

Citation: Report may/2014, PESC-COPPE, Federal University of Rio de Janeiro.

Download: [PDF]

Entry Submitted: 05/28/2014
Entry Accepted: 05/28/2014
Entry Last Modified: 10/01/2014

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