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A stochastic primal-dual algorithm for composite optimization with a linear operator
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DOI：10.1016/j.eswa.2024.126021.png)
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Abstract
En 中文
This paper introduces a stochastic primal-dual algorithm tailored for solving optimization problems involving the sum of three functions with a linear operator. Additionally, we conduct a comprehensive analysis of the convergence of our proposed algorithm within a generally convex framework. Our study includes numerical experiments focusing on fused logistic regression and graph-guided regularized logistic regression problems. The results demonstrate that our algorithm outperforms other state-of-the-art methods in terms of efficiency and consistency.
Keywords:
Compositely optimization
Primal-dual algorithm
Stochastic algorithm
Three-operator splitting
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W