Quasi-monotone Subgradient Methods for Nonsmooth Convex Minimization

Yu. Nesterov () and V. Shikhman ()
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Yu. Nesterov: Center for Operations Research and Econometrics (CORE)
V. Shikhman: Center for Operations Research and Econometrics (CORE)

Journal of Optimization Theory and Applications, 2015, vol. 165, issue 3, No 13, 917-940

Abstract: Abstract In this paper, we develop new subgradient methods for solving nonsmooth convex optimization problems. These methods guarantee the best possible rate of convergence for the whole sequence of test points. Our methods are applicable as efficient real-time stabilization tools for potential systems with infinite horizon. Preliminary numerical experiments confirm a high efficiency of the new schemes.

Keywords: Convex optimization; Nonsmooth optimzation; Subgradient methods; Rate of convergence; Primal-dual methods; 90C25; 90C47; 68Q25 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10957-014-0677-5

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