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Smooth minimization of non-smooth functions

Published: 01 May 2005 Publication History

Abstract

In this paper we propose a new approach for constructing efficient schemes for non-smooth convex optimization. It is based on a special smoothing technique, which can be applied to functions with explicit max-structure. Our approach can be considered as an alternative to black-box minimization. From the viewpoint of efficiency estimates, we manage to improve the traditional bounds on the number of iterations of the gradient schemes from ** keeping basically the complexity of each iteration unchanged.

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  • (2024)DataSPProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702775(2094-2112)Online publication date: 15-Jul-2024
  • (2024)Efficient algorithms for empirical group distributionally robust optimization and beyondProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694437(57384-57414)Online publication date: 21-Jul-2024
  • (2024)Uniformly stable algorithms for adversarial training and beyondProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694302(54319-54340)Online publication date: 21-Jul-2024
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Published In

cover image Mathematical Programming: Series A and B
Mathematical Programming: Series A and B  Volume 103, Issue 1
May 2005
200 pages

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 May 2005

Author Tags

  1. Complexity theory
  2. Convex optimization
  3. Non-smooth optimization
  4. Optimal methods
  5. Structural optimization

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Cited By

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  • (2024)DataSPProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702775(2094-2112)Online publication date: 15-Jul-2024
  • (2024)Efficient algorithms for empirical group distributionally robust optimization and beyondProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694437(57384-57414)Online publication date: 21-Jul-2024
  • (2024)Uniformly stable algorithms for adversarial training and beyondProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694302(54319-54340)Online publication date: 21-Jul-2024
  • (2024)Universal gradient methods for stochastic convex optimizationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693804(42620-42646)Online publication date: 21-Jul-2024
  • (2024)Smooth Tchebycheff scalarization for multi-objective optimizationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693297(30479-30509)Online publication date: 21-Jul-2024
  • (2024)Demystifying SGD with doubly stochastic gradientsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693040(24210-24247)Online publication date: 21-Jul-2024
  • (2024)A universal transfer theorem for convex optimization algorithms using inexact first-order oraclesProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693016(23532-23546)Online publication date: 21-Jul-2024
  • (2024)Private heterogeneous federated learning without a trusted server revisitedProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692660(14763-14789)Online publication date: 21-Jul-2024
  • (2024)Fast algorithms for hypergraph pagerank with applications to semi-supervised learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692125(1306-1330)Online publication date: 21-Jul-2024
  • (2024)Exploring the inefficiency of heavy ball as momentum parameter approaches 1Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/431(3899-3907)Online publication date: 3-Aug-2024
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