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A Compressed Gradient Tracking Method for Decentralized Optimization With Linear Convergence
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AI
DOI：10.1109/TAC.2022.3180695.png)
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摘要
En 中文
Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minimize the average of local cost functions over a multiagent network using only local computation and peer-to-peer communication. In this article, we propose a novel compressed gradient tracking algorithm (C-GT) that combines gradient tracking technique with communication compression. In particular, C-GT is compatible with a general class of compression operators that unifies both unbiased and biased compressors. We show that C-GT inherits the advantages of gradient tracking-based algorithms and achieves linear convergence rate for strongly convex and smooth objective functions. Numerical examples complement the theoretical findings and demonstrate the efficiency and flexibility of the proposed algorithm.
Keyword:
Linear programming
Convergence
Compressors
Quantization (signal)
Cost function
Robots
Radio frequency
Communication compression
decentralized optimization
gradient tracking
linear convergence
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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