Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling

The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly non-smooth) convex functions using only local computation and communication. It arises in various application domains, including distributed tracking and localization, multiagent co-ordination, estimation in sensor networks, and large-scale machine learning. The authors develop and analyze distributed algorithms based on dual subgradient averaging, and they provide sharp bounds on their convergence rates as a function of the network size and topology.

Provided by: Institute of Electrical and Electronics Engineers Topic: Networking Date Added: Jun 2011 Format: PDF

Find By Topic