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- W2522803573 abstract "This paper considers the problems of unconstrained minimization of large scale smooth convex functions having block-coordinate-wise Lipschitz continuous gradients. The block coordinate descent (BCD) method are among the first optimization schemes suggested for solving such problems cite{nesterov2012efficiency}. We obtain a new lower (to our best knowledge the lowest currently) bound that is $16p^3$ times smaller than the best known on the information-based complexity of BCD method based on an effective technique called Performance Estimation Problem (PEP) proposed by Drori and Teboulle cite{drori2012performance} recently for analyzing the performance of first-order black box optimization methods. Numerical test confirms our analysis." @default.
- W2522803573 created "2016-09-30" @default.
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- W2522803573 date "2016-08-16" @default.
- W2522803573 modified "2023-10-16" @default.
- W2522803573 title "A better convergence analysis of the block coordinate descent method for large scale machine learning" @default.
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- W2522803573 doi "https://doi.org/10.48550/arxiv.1608.04826" @default.
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