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Distributed optimization for multi-task learning via nuclear-norm approximation
D. Mateos-Nuñez, J. Cortés
IFAC Workshop on Distributed Estimation and Control in Networked Systems, Philadelphia, Pennsylvania, USA, 2015, vol. 48, issue 22, pp. 64-69

Abstract

We exploit a variational characterization of the nuclear norm to extend the framework of distributed convex optimization to machine learning problems that focus on the sparsity of the aggregate solution. We propose two distributed dynamics that can be used for multi-task feature learning and recommender systems in scenarios with more tasks or users than features. Our first dynamics tackles a convex minimization on local decision variables subject to agreement on a set of local auxiliary matrices. Our second dynamics employs a saddle-point reformulation through Fenchel conjugation of quadratic forms, avoiding the computation of the inverse of the local matrices. We establish the correctness of both coordination algorithms using a general analytical framework developed in our previous work that combines distributed optimization and subgradient methods for saddle-point problems.

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Mechanical and Aerospace Engineering, University of California, San Diego
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