The Efficiency of subgradient projection methods for convex nondifferentiable optimization

Krzysztof C. Kiwiel 1
1 PROMATH - Mathematical Programming
Inria Paris-Rocquencourt
Abstract : We study subgradient methods for convex optimization that use projections onto successive approximations of level sets of the objective corresponding to estimates of the optimal value. We show that they enjoy almost optimal efficiency estimates. We present several variants, establish their efficiency estimates and discuss possible implementations. In particular, their projection subproblems may be solved inexactly via relaxation methods, thus opening the way for parallel implementations. We discuss accelerations of relaxation methods based on simultaneous projections, surrogate constraints and conjugate and projected (conditional) subgradient techniques.
Type de document :
Rapport
[Research Report] RR-1845, INRIA. 1993
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https://hal.inria.fr/inria-00074827
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Soumis le : mercredi 24 mai 2006 - 16:30:41
Dernière modification le : vendredi 25 mai 2018 - 12:02:05
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Krzysztof C. Kiwiel. The Efficiency of subgradient projection methods for convex nondifferentiable optimization. [Research Report] RR-1845, INRIA. 1993. 〈inria-00074827〉

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