# Separable Approximations and Decomposition Methods for the Augmented Lagrangian

Rachael Tappenden, Peter Richtarik, Burak Buke

Research output: Working paper

## Abstract

In this paper we study decomposition methods based on separable approximations for minimizing the augmented Lagrangian. In particular, we study and compare the Diagonal Quadratic Approximation Method (DQAM) of Mulvey and Ruszczy\'{n}ski and the Parallel Coordinate Descent Method (PCDM) of Richt\'arik and Tak\'a\v{c}. We show that the two methods are equivalent for feasibility problems up to the selection of a single step-size parameter. Furthermore, we prove an improved complexity bound for PCDM under strong convexity, and show that this bound is at least $8(L'/\bar{L})(\omega-1)^2$ times better than the best known bound for DQAM, where $\omega$ is the degree of partial separability and $L'$ and $\bar{L}$ are the maximum and average of the block Lipschitz constants of the gradient of the quadratic penalty appearing in the augmented Lagrangian.
Original language English ArXiv Published - 30 Aug 2013

• math.OC
• cs.DC
• cs.NA
• stat.ML

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• ### Science and Innovation: Numerical Algorithms and Intelligent Software for the Evolving HPC Platform

Leimkuhler, B.

EPSRC

1/08/0931/07/14

Project: Research

• ### Separable approximations and decomposition methods for the augmented Lagrangian

Tappenden, R., Richtarik, P. & Buke, B., 6 Nov 2014, 30, 3, p. 643-668

Research output: Contribution to journalArticlepeer-review

Open Access
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