Modified Accelerated Bundle-Level Methods and Their Application in Two-Stage Stochastic Programming
作者： Chunming Tang , Bo He , Zhenzhen Wang
DOI: 10.3390/MATH8020265
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摘要: 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) structure non-smooth CP problems. In this paper, we propose two modified bundle-level type methods, namely, the APL (MAPL) USL (MUSL) methods. Compared with original MAPL MUSL reduce number of subproblems one in each iteration, thereby improving efficiency algorithms. Conclusions iteration algorithms are established. Furthermore, applied to two-stage stochastic programming, numerical experiments implemented illustrate advantages our terms accuracy.
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