资源分类
》 资源 》论文》Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks

Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks

2020-03-10 | |  64 |   35 |   0

Abstract

In this paper we consider nonconvex optimization and learning over a network of distributed nodes. We develop a Proximal Primal-Dual Algorithm (Prox-PDA), which enables the network nodes to distributedly and collectively compute the set of first-order stationary solutions in a global sublinear manner [with a rate of O(1/r), where r is the iteration counter]. To the best of our knowledge, this is the first algorithm that enables distributed nonconvex optimization with global sublinear rate guarantees. Our numerical experiments also demonstrate the effectiveness of the proposed algorithm.

上一篇：Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery

下一篇：Convolutional Sequence to Sequence Learning

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...