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Modified Accelerated Bundle-Level Methods and Their Application in Two-Stage Stochastic Programming

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  • Chunming Tang

    (College of Mathematics and Information Science, Guangxi University, Nanning 540004, China)

  • Bo He

    (College of Mathematics and Information Science, Guangxi University, Nanning 540004, China)

  • Zhenzhen Wang

    (College of Mathematics and Information Science, Guangxi University, Nanning 540004, China)

Abstract

The accelerated prox-level (APL) and uniform smoothing level (USL) methods recently proposed by Lan (Math Program, 149: 1–45, 2015) can achieve uniformly optimal complexity when solving black-box convex programming (CP) and structure non-smooth CP problems. In this paper, we propose two modified accelerated bundle-level type methods, namely, the modified APL (MAPL) and modified USL (MUSL) methods. Compared with the original APL and USL methods, the MAPL and MUSL methods reduce the number of subproblems by one in each iteration, thereby improving the efficiency of the algorithms. Conclusions of optimal iteration complexity of the proposed algorithms are established. Furthermore, the modified methods are applied to the two-stage stochastic programming, and numerical experiments are implemented to illustrate the advantages of our methods in terms of efficiency and accuracy.

Suggested Citation

  • Chunming Tang & Bo He & Zhenzhen Wang, 2020. "Modified Accelerated Bundle-Level Methods and Their Application in Two-Stage Stochastic Programming," Mathematics, MDPI, vol. 8(2), pages 1-26, February.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:265-:d:321548
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    References listed on IDEAS

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