Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms. Revision
This document presents a model for asynchronous distributed computation and then proceed to analyze the convergence of natural asynchronous distributed versions of a large class of deterministic and stochastic gradient-like algorithms. It is shown that such algorithms retain the desirable convergence properties of their centralized counterparts, provided that the time between consecutive communications between processors and communication delays are not too large. Additional keywords: Message processing, Mathematical models, Coefficients. (Author).
"This document presents a model for asynchronous distributed computation and then proceed to analyze the convergence of natural asynchronous distributed versions of a large class of deterministic and stochastic gradient-like algorithms. It is shown that such algorithms retain the desirable convergence properties of their centralized counterparts, provided that the time between consecutive communications between processors and communication delays are not too large. Additional keywords: Message processing, Mathematical models, Coefficients. (Author)."@en
MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS.
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