A proximal multiplier method for separable convex minimization
Orlando Sarmiento (osarmientocos.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.
Entry Submitted: 05/28/2014
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