What question did this study set out to answer?

We aim to analyze the convergence of asynchronous distributed gradient optimization algorithms.

September 1, 1986IEEE Transactions on Automatic Control2,037 citations

Distributed asynchronous deterministic and stochastic gradient optimization algorithms

JTJohn N. Tsitsiklis DBDimitri P. Bertsekas MAMichael Athans

Key Points

We aim to analyze the convergence of asynchronous distributed gradient optimization algorithms.
Developed a model for asynchronous distributed computation.
Analyzed the convergence of various deterministic and stochastic gradient-like algorithms.
Considered factors such as communication delays and interprocessor communication times.
These asynchronous distributed algorithms maintain convergence properties akin to centralized algorithms.
Performance remains effective if communication delays are manageable.

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 interprocessor communications and the communication delays are not too large.

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Cite This Study

Tsitsiklis et al. (1986) studied this question.

synapsesocial.com/papers/6a16207ae40e368638949cca https://doi.org/10.1109/tac.1986.1104412

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September 1, 1986IEEE Transactions on Automatic Control2,037 citations

Distributed asynchronous deterministic and stochastic gradient optimization algorithms

View Full Paper

JTJohn N. Tsitsiklis DBDimitri P. Bertsekas MAMichael Athans

Key Points

We aim to analyze the convergence of asynchronous distributed gradient optimization algorithms.
Developed a model for asynchronous distributed computation.
Analyzed the convergence of various deterministic and stochastic gradient-like algorithms.
Considered factors such as communication delays and interprocessor communication times.
These asynchronous distributed algorithms maintain convergence properties akin to centralized algorithms.
Performance remains effective if communication delays are manageable.

Abstract

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Cite This Study

Tsitsiklis et al. (1986) studied this question.

synapsesocial.com/papers/6a16207ae40e368638949cca https://doi.org/10.1109/tac.1986.1104412

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