Mirror Prox algorithm for multi-term composite minimization and semi-separable problems
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DOI: 10.1007/s10589-014-9723-3
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- NESTEROV, Yurii, 2013. "Gradient methods for minimizing composite functions," LIDAM Reprints CORE 2510, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Arkadi Nemirovski & Reuven Y. Rubinstein, 2002. "An Efficient Stochastic Approximation Algorithm for Stochastic Saddle Point Problems," International Series in Operations Research & Management Science, in: Moshe Dror & Pierre L’Ecuyer & Ferenc Szidarovszky (ed.), Modeling Uncertainty, chapter 0, pages 156-184, Springer.
- Arkadi Nemirovski & Shmuel Onn & Uriel G. Rothblum, 2010. "Accuracy Certificates for Computational Problems with Convex Structure," Mathematics of Operations Research, INFORMS, vol. 35(1), pages 52-78, February.
- NESTEROV, Yu., 2005. "Smooth minimization of non-smooth functions," LIDAM Reprints CORE 1819, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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Cited by:
- E. M. Bednarczuk & A. Jezierska & K. E. Rutkowski, 2018. "Proximal primal–dual best approximation algorithm with memory," Computational Optimization and Applications, Springer, vol. 71(3), pages 767-794, December.
- Jueyou Li & Guo Chen & Zhaoyang Dong & Zhiyou Wu, 2016. "A fast dual proximal-gradient method for separable convex optimization with linear coupled constraints," Computational Optimization and Applications, Springer, vol. 64(3), pages 671-697, July.
- Erfan Yazdandoost Hamedani & Afrooz Jalilzadeh, 2023. "A stochastic variance-reduced accelerated primal-dual method for finite-sum saddle-point problems," Computational Optimization and Applications, Springer, vol. 85(2), pages 653-679, June.
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More about this item
Keywords
Numerical algorithms for variational problems; Composite optimization; Minimization problems with multi-term penalty; Proximal methods; 65K10; 65K05; 90C06; 90C25; 90C47;All these keywords.
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