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arXiv · 1512.04011

L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework

Virginia Smith·Simone Forte·Michael I. Jordan·Martin Jaggi
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Abstract

Despite the importance of sparsity in many large-scale applications, there are few methods for distributed optimization of sparsity-inducing objectives. In this paper, we present a communication-efficient framework for L1-regularized optimization in the distributed environment. By viewing classical objectives in a more general primal-dual setting, we develop a new class of methods that can be efficiently distributed and applied to common sparsity-inducing models, such as Lasso, sparse logistic regression, and elastic net-regularized problems. We provide theoretical convergence guarantees for our framework, and demonstrate its efficiency and flexibility with a thorough experimental comparison on Amazon EC2. Our proposed framework yields speedups of up to 50x as compared to current state-of-the-art methods for distributed L1-regularized optimization.

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Virginia Smith, Simone Forte, Michael I. Jordan, Martin Jaggi. 2016-06-02. L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework. https://arxiv.org/abs/1512.04011

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