Distributed Majorization-Minimization for Laplacian Regularized Problems
Optimization and Control
2018-04-02 v2
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.
Cite
@article{arxiv.1803.10317,
title = {Distributed Majorization-Minimization for Laplacian Regularized Problems},
author = {Jonathan Tuck and David Hallac and Stephen Boyd},
journal= {arXiv preprint arXiv:1803.10317},
year = {2018}
}
Comments
18 pages, 3 figures