Distributed Stochastic Gradient Tracking Algorithm With Variance Reduction for Non-Convex Optimization

Distributed Stochastic Gradient Tracking Algorithm With Variance Reduction for Non-Convex Optimization

This article proposes a distributed stochastic algorithm with variance reduction for general smooth non-convex finite-sum optimization, which has wide applications in signal processing and machine learning communities. In distributed setting, a large number of samples are allocated to multiple agent...

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Bibliographic Details
Main Authors: Chen, J. (Author), Jiang, X. (Author), Sun, J. (Author), Zeng, X. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
Complex networks
Complexity analysis
Convergence
Convex optimization
distributed algorithm
Distributed algorithms
Distributed database
Distributed databases
Finite sums
Machine learning
non-convex finite-sum optimization
Non-convex finite-sum optimization
Optimisations
Optimization
Parallel algorithms
Random variables
Signal processing
Signal processing algorithms
stochastic gradient
Stochastic gradient
Stochastic systems
Tracking (position)
variance reduction.
Variance reduction.
Variance reductions
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