A stochastic primal–dual algorithm for composite optimization with a linear operator - Wen, Meng; Zhang, Yongqiang; Tang, Yuchao; Cui, Angang; Peng, Jigen (Expert systems with applications, 2025)
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. •Design a new stochastic primal–dual algorithm for solving Non-smooth optimization problems.•Conduct a comprehensive analysis of the convergence of our proposed algorithm.•Illustrate the efficiency of our proposed algorithm through some numerical example.