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Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms

John N. Tsitsiklis, Dimitri P. Bertsekas, Michael Athans

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Abstract

We present 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. We show that such algorithms retain the desirable convergence properties of their centralized counterparts, provided that the time between consecutive communications between processors plus communication delays are not too large.

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What this paper is about

We present 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. We show that such algorithms retain the desirable convergence properties of their centralized counterparts, provided that the time between consecutive communications between processors plus communication delays are not too large.

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OpenAlex reports 80 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

We present 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. We show that such algorithms retain the desirable convergence properties of their centralized counterparts, provided that the time between consecutive communications between processors plus communication delays are not too large.

Key concepts: Asynchronous communication, Computer science, Convergence (economics), Distributed algorithm, Computation, Class (philosophy), Distributed computing, Algorithm

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