تقرير
A Stochastic Second-Order Proximal Method for Distributed Optimization
| العنوان: | A Stochastic Second-Order Proximal Method for Distributed Optimization |
|---|---|
| المؤلفون: | Qiu, Chenyang, Zhu, Shanying, Ou, Zichong, Lu, Jie |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control |
| الوصف: | In this paper, we propose a distributed stochastic second-order proximal method that enables agents in a network to cooperatively minimize the sum of their local loss functions without any centralized coordination. The proposed algorithm, referred to as St-SoPro, incorporates a decentralized second-order approximation into an augmented Lagrangian function, and then randomly samples the local gradients and Hessian matrices of the agents, so that it is computationally and memory-wise efficient, particularly for large-scale optimization problems. We show that for globally restricted strongly convex problems, the expected optimality error of St-SoPro asymptotically drops below an explicit error bound at a linear rate, and the error bound can be arbitrarily small with proper parameter settings. Simulations over real machine learning datasets demonstrate that St-SoPro outperforms several state-of-the-art distributed stochastic first-order methods in terms of convergence speed as well as computation and communication costs. Comment: 6 pages, 8 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.10591 |
| رقم الأكسشن: | edsarx.2211.10591 |
| قاعدة البيانات: | arXiv |
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