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• W4385371323 abstract "We consider a fundamental problem in the field of machine learning-structural risk minimization, which can be represented as the average of a large number of smooth component functions plus a simple and convex (but possibly non-smooth) function. In this paper, we propose a novel proximal variance reducing stochastic method building on the introduced Point-SAGA. Our method achieves two proximal operator calculations by combining the fast Douglas–Rachford splitting and refers to the scheme of the FISTA algorithm in the choice of momentum factors. We show that the objective function value converges to the iteration point at the rate of <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$mathcal{O}(1/k)$</tex> when each loss function is convex and smooth. In addition, we prove that our method achieves a linear convergence rate for strongly convex and smooth loss functions. Experiments demonstrate the effectiveness of the proposed algorithm, especially when the loss function is ill-conditioned with good acceleration." @default.
• W4385371323 created "2023-07-29" @default.
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• W4385371323 date "2023-12-01" @default.
• W4385371323 modified "2023-09-24" @default.
• W4385371323 title "A Variance Reducing Stochastic Proximal Method with Acceleration Techniques" @default.
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• W4385371323 doi "https://doi.org/10.26599/tst.2022.9010051" @default.
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