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

Chunming Tang, Bo He and Zhenzhen Wang
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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

Mathematics, 2020, vol. 8, issue 2, 1-26

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.

Keywords: stochastic programming; multi-step accelerated scheme; bundle method; complexity analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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