Distributed Majorization-Minimization for Laplacian Regularized Problems

2018/03/27 by Jonathan Tuck, Tuck, Jonathan, David Hallac +3 · 1 citation
Mathematics · #FOS: Mathematics #Optimization and Control (math.OC) #math.OC

paper · pdf · doi:10.48550/arxiv.1803.10317

18 pages, 3 figures

arxiv created 2018/03/30 · arxiv updated 2018/04/02

Abstract

We consider the problem of minimizing a block separable convex function (possibly nondifferentiable, and including constraints) plus Laplacian regularization, a problem that arises in applications including model fitting, regularizing stratified models, and multi-period portfolio optimization. We develop a distributed majorization-minimization method for this general problem, and derive a complete, self-contained, general, and simple proof of convergence. Our method is able to scale to very large problems, and we illustrate our approach on two applications, demonstrating its scalability and accuracy.

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