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- W2073750241 abstract "We consider the minimization of a sum $${sum_{i=1}^mf_i(x)}$$ consisting of a large number of convex component functions f i . For this problem, incremental methods consisting of gradient or subgradient iterations applied to single components have proved very effective. We propose new incremental methods, consisting of proximal iterations applied to single components, as well as combinations of gradient, subgradient, and proximal iterations. We provide a convergence and rate of convergence analysis of a variety of such methods, including some that involve randomization in the selection of components. We also discuss applications in a few contexts, including signal processing and inference/machine learning." @default.
- W2073750241 created "2016-06-24" @default.
- W2073750241 creator A5039911471 @default.
- W2073750241 date "2011-06-11" @default.
- W2073750241 modified "2023-09-27" @default.
- W2073750241 title "Incremental proximal methods for large scale convex optimization" @default.
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- W2073750241 doi "https://doi.org/10.1007/s10107-011-0472-0" @default.
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