On Simplifying the Primal-Dual Method of Multipliers

Conference paper (2016)

Authors

G. Zhang Signal Processing Systems - EEMCS
R. Heusdens Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2016 G. Zhang, R. Heusdens
DOI: https://doi.org/10.1109/icassp.2016.7472594
SVM Distributed optimization ADMM PDMM
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Published Date

19-05-2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

Recently, the primal-dual method of multipliers (PDMM) has been proposed to solve a convex optimization problem defined over a general graph. In this paper, we consider simplifying PDMM for a subclass of the convex optimization problems. This subclass includes the consensus problem as a special form. By using algebra, we show that the update expressions of PDMM can be simplified significantly. We then evaluate PDMM for training a support vector machine (SVM). The experimental results indicate that PDMM converges considerably faster than the alternating direction method of multipliers (ADMM).