A Multi-Scale Method for Distributed Convex Optimization with Constraints

TL;DR

This paper introduces a multi-scale continuous-time distributed algorithm for constrained convex optimization that effectively handles stochastic network switching and communication noise, improving analysis and simulation efficiency.

Contribution

It develops a smooth, multi-scale method for distributed constrained convex optimization over stochastic, switching networks, extending existing algorithms to more realistic scenarios.

Findings

Handles stochastic network switching and communication noise.

Provides a smooth optimization dynamics for easier analysis.

Generalizes existing algorithms to stochastic, switching networks.

Abstract

This paper proposes a multi-scale method to design a continuous-time distributed algorithm for constrained convex optimization problems by using multi-agents with Markov switched network dynamics and noisy inter-agent communications. Unlike most previous work which mainly puts emphasis on dealing with fixed network topology, this paper tackles the challenging problem of investigating the joint effects of stochastic networks and the inter-agent communication noises on the distributed optimization dynamics, which has not been systemically studied in the past literature. Also, in sharp contrast to previous work in constrained optimization, we depart from the use of projected gradient flow which is non-smooth and hard to analyze; instead, we design a smooth optimization dynamics which leads to easier convergence analysis and more efficient numerical simulations. Moreover, the multi-scale method presented in this paper generalizes previously known distributed convex optimization algorithms from the fixed network topology to the switching case and the stochastic averaging obtained in this paper is a generalization of the existing deterministic averaging.