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Distributed asynchronous deterministic and stochastic gradient optimization algorithms

IEEE Transactions on Automatic Control · 1986 · Vol. 31(9) · pp. 803–812

John N. Tsitsiklis✉(Massachusetts Institute of Technology)Dimitri P. Bertsekas(Massachusetts Institute of Technology)Michael Athans(Massachusetts Institute of Technology)

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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 interprocessor communications and the communication delays are not too large.

Stochastic Gradient Optimization Techniques Sparse and Compressive Sensing Techniques Privacy-Preserving Technologies in DataAsynchronous communicationConvergence (economics)Computer scienceComputationDistributed algorithmStochastic approximationStochastic processAlgorithmDistributed computingMathematical optimization

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