On Simplifying the Primal-Dual Method of Multipliers
Guoqiang Zhang (TU Delft - Signal Processing Systems)
Richard Heusdens (TU Delft - Signal Processing Systems)
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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).