Adaptive Penalty-Based Distributed Stochastic Convex Optimization

• Published in: IEEE transactions on signal processing Vol. 62; no. 15; pp. 3924 - 3938
• Main Authors: Towfic, Zaid J., Sayed, Ali H.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptation and learning; Algorithms; Applied sciences; Approximation algorithms; Approximation methods; consensus strategies; Constants; constrained optimization; Convex functions; Cost engineering; Cost function; Detection, estimation, filtering, equalization, prediction; diffusion strategies; distributed processing; Exact sciences and technology; Information, signal and communications theory; Networks; Optimization; penalty method; Processors; Signal and communications theory; Signal processing algorithms; Signal, noise; Strategy; Telecommunications and information theory; Target tracking; Adaptation and learning; Adaptive algorithm; diffusion strategies; Network architecture; Stochastic method; Convex programming; Penalty method; Constrained optimization; Learning; Topological structure; Distributed processing; Optimal solution; Distributed algorithm; Signal processing; Cost function; consensus strategies; Convex function; Localization

In this work, we study the task of distributed optimization over a network of learners in which each learner possesses a convex cost function, a set of affine equality constraints, and a set of convex inequality constraints. We propose a fully distributed adaptive diffusion algorithm based on penalt...