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Advances in Asynchronous Parallel and Distributed Optimization
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DOI：10.1109/JPROC.2020.3026619.png)
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Abstract
En 中文
Motivated by large-scale optimization problems arising in the context of machine learning, there have been several advances in the study of asynchronous parallel and distributed optimization methods during the past decade. Asynchronous methods do not require all processors to maintain a consistent view of the optimization variables. Consequently, they generally can make more efficient use of computational resources than synchronous methods, and they are not sensitive to issues like stragglers (i.e., slow nodes) and unreliable communication links. Mathematical modeling of asynchronous methods involves proper accounting of information delays, which makes their analysis challenging. This article reviews recent developments in the design and analysis of asynchronous optimization methods, covering both centralized methods, where all processors update a master copy of the optimization variables, and decentralized methods, where each processor maintains a local copy of the variables. The analysis provides insights into how the degree of asynchrony impacts convergence rates, especially in stochastic optimization methods.
Keywords:
Program processors
Optimization methods
Machine learning
Computational modeling
Convergence
Computational efficiency
Distributed algorithms
machine learning
machine learning algorithms
optimization methods
parallel algorithms
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