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• W2949802012 abstract "In this paper we analyze several new methods for solving nonconvex optimization problems with the objective function formed as a sum of two terms: one is nonconvex and smooth, and another is convex but simple and its structure is known. Further, we consider both cases: unconstrained and linearly constrained nonconvex problems. For optimization problems of the above structure, we propose random coordinate descent algorithms and analyze their convergence properties. For the general case, when the objective function is nonconvex and composite we prove asymptotic convergence for the sequences generated by our algorithms to stationary points and sublinear rate of convergence in expectation for some optimality measure. Additionally, if the objective function satisfies an error bound condition we derive a local linear rate of convergence for the expected values of the objective function. We also present extensive numerical experiments for evaluating the performance of our algorithms in comparison with state-of-the-art methods." @default.
• W2949802012 created "2019-06-27" @default.
• W2949802012 creator A5019114471 @default.
• W2949802012 creator A5049988389 @default.
• W2949802012 date "2013-05-17" @default.
• W2949802012 modified "2023-09-24" @default.
• W2949802012 title "Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization" @default.
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• W2949802012 doi "https://doi.org/10.48550/arxiv.1305.4027" @default.
• W2949802012 hasPublicationYear "2013" @default.
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