DeepAI AI Chat
Log In Sign Up

Distributed Distributionally Robust Optimization with Non-Convex Objectives

10/14/2022
by   Yang Jiao, et al.
0

Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. However, existing DRO techniques face three key challenges: 1) how to deal with the asynchronous updating in a distributed environment; 2) how to leverage the prior distribution effectively; 3) how to properly adjust the degree of robustness according to different scenarios. To this end, we propose an asynchronous distributed algorithm, named Asynchronous Single-looP alternatIve gRadient projEction (ASPIRE) algorithm with the itErative Active SEt method (EASE) to tackle the distributed distributionally robust optimization (DDRO) problem. Furthermore, a new uncertainty set, i.e., constrained D-norm uncertainty set, is developed to effectively leverage the prior distribution and flexibly control the degree of robustness. Finally, our theoretical analysis elucidates that the proposed algorithm is guaranteed to converge and the iteration complexity is also analyzed. Extensive empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, and remain robust against data heterogeneity as well as malicious attacks, but also tradeoff robustness with performance.

READ FULL TEXT
06/07/2018

Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization

Recent studies have illustrated that stochastic gradient Markov Chain Mo...
10/24/2017

Asynchronous ADMM for Distributed Non-Convex Optimization in Power Systems

Large scale, non-convex optimization problems arising in many complex ne...
12/20/2022

Asynchronous Distributed Bilevel Optimization

Bilevel optimization plays an essential role in many machine learning ta...
06/08/2020

A Stochastic Subgradient Method for Distributionally Robust Non-Convex Learning

We consider a distributionally robust formulation of stochastic optimiza...
11/07/2022

Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification

In the field of reinforcement learning, because of the high cost and ris...
04/09/2014

A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning

Learning sparse combinations is a frequent theme in machine learning. In...
01/15/2021

CPU Scheduling in Data Centers Using Asynchronous Finite-Time Distributed Coordination Mechanisms

We propose an asynchronous iterative scheme which allows a set of interc...