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- W4286231825 abstract "This paper considers optimization problems where the objective is the sum of a function given by an expectation and a closed convex composite function, and proposes stochastic composite proximal bundle (SCPB) methods for solving it. Complexity guarantees are established for them without requiring knowledge of parameters associated with the problem instance. Moreover, it is shown that they have optimal complexity when these problem parameters are known. To the best of our knowledge, this is the first proximal bundle method for stochastic programming able to deal with continuous distributions. Finally, we present computational results showing that SCPB substantially outperforms the robust stochastic approximation (RSA) method in all instances considered." @default.
- W4286231825 created "2022-07-21" @default.
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- W4286231825 date "2022-07-18" @default.
- W4286231825 modified "2023-10-17" @default.
- W4286231825 title "A single cut proximal bundle method for stochastic convex composite optimization" @default.
- W4286231825 doi "https://doi.org/10.48550/arxiv.2207.09024" @default.
- W4286231825 hasPublicationYear "2022" @default.
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