- Convergence rate of a proximal multiplier algorithm for separable convex minimization Orlando Sarmiento (osarmientocos.ufrj.br) Erik Papa Quiroz (erikpapagmail.com) Paulo Oliveira (oliveira.paulorobertogmail.com) Abstract: The proximal multiplier method with proximal distances (PMAPD) proposed by O. Sarmiento C., E. A. Papa Quiroz and P. R. Oliveira, applied to solve a convex program with separable structure unified the works of Chen and Teboulle (PCPM method), Kyono and Fukushima (NPCPMM) and Auslender and Teboulle (EPDM) and extended the convergence properties for the class of $\varphi-$divergence distances. In this paper, we show that under standard assumptions the iterations generated by the (PMAPD) converge linearly to the unique optimal solution of the problem. Keywords: Proximal multiplier method; separable convex problem; proximal distances Category 1: Convex and Nonsmooth Optimization (Convex Optimization ) Citation: Report2-PRO, PESC-COPPE-UFRJ Download: [PDF]Entry Submitted: 03/13/2015Entry Accepted: 03/13/2015Entry Last Modified: 06/24/2015Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.