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• W4313367297 startingPage "108889" @default.
• W4313367297 abstract "A plethora of problems arising in signal processing, machine learning and statistics can be cast as large-scale optimization problems with a composite objective structure. Such problems are typically solved by utilizing iterative first-order algorithms. In this work, we devise a new accelerated gradient-based estimating sequence technique for solving large-scale optimization problems with composite objective structure. Specifically, we introduce a new class of estimating functions, which are obtained by utilizing both a tight lower bound on the objective function, as well as the gradient mapping technique. Then, using the proposed estimating functions, we construct a class of Composite Objective Multi-step Estimating-sequence Techniques (COMET), which are endowed with an efficient line-search procedure. We prove that our proposed COMET enjoys the accelerated convergence rate, and our newly established convergence results allow for step-size adaptation. Our theoretical findings are supported by extensive computational experiments on various problem types and real-world datasets. Moreover, our numerical results show evidence of the robustness of the proposed method to the imperfect knowledge of the smoothness and strong convexity parameters." @default.
• W4313367297 created "2023-01-06" @default.
• W4313367297 creator A5000676115 @default.
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• W4313367297 date "2023-05-01" @default.
• W4313367297 modified "2023-09-25" @default.
• W4313367297 title "A new class of composite objective multistep estimating sequence techniques" @default.
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• W4313367297 doi "https://doi.org/10.1016/j.sigpro.2022.108889" @default.
• W4313367297 hasPublicationYear "2023" @default.
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