Abstract
Finite sum minimization has many applications in machine learning. Due to its wide applications, people have proposed many variation reduction algorithms to solve the finite minimization problems. Recently, some studies have shown that these variance reduction algorithms can also be extended to solve more general problems, i.e., monotone inclusion problem. In this paper, we propose a new algorithm that improves some traditional variance reduction algorithms and is able to solve general monotone inclusion problems. The new algorithm has a linear convergence and the worth of the new algorithm is additionally proved by numerical experiments.
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