Výsledky vyhľadávania - "stochastic mirror descent algorithm"

Almost Sure Convergence and Non-Asymptotic Concentration Bounds for Stochastic Mirror Descent Algorithm

Autori: Anik Kumar Paul Arun D. Mahindrakar Rachel K. Kalaimani

Zdroj: IEEE Control Systems Letters. 8:2397-2402

Predmety: Optimization and Control (math.OC), FOS: Mathematics, Mathematics - Optimization and Control

Prístupová URL adresa: http://arxiv.org/abs/2407.05863

General inertial proximal stochastic mirror descent algorithm beyond Lipschitz smoothness assumption

Autori: Shuang Wang Xiaomei Dong Xue Gao

Zdroj: Journal of Computational and Applied Mathematics. :117108

Broadcast-based asynchronous convex optimization using quantized distributed stochastic mirror descent algorithm

Autori: Xianju Fang Baoyong Zhang Deming Yuan a ďalší

Zdroj: Systems & Control Letters. 203:106159

Adaptive quantized online distributed stochastic mirror descent algorithm

Autori: Junru Xie Wenhua Gao Yibin Xie

Zdroj: 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC). :671-676

Predmety: 0209 industrial biotechnology, 02 engineering and technology

A stochastic mirror-descent algorithm for solving AXB=C over an multi-agent system

Autori: Songsong Cheng Yinghui Wang

Zdroj: Kybernetika. :256-271

Predmety: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: https://www.kybernetika.cz/content/2021/2/256/paper.pdf
https://dblp.uni-trier.de/db/journals/kybernetika/kybernetika57.html#WangC21

Distributed stochastic mirror descent algorithm for resource allocation problem

Autori: Zhipeng Tu Huashu Qin Yinghui Wang

Zdroj: Control Theory and Technology. 18:339-347

Predmety: 0209 industrial biotechnology, 0211 other engineering and technologies, 02 engineering and technology, 12. Responsible consumption

Prístupová URL adresa: https://link.springer.com/article/10.1007/s11768-020-00018-8

Distributed Stochastic Mirror Descent Algorithm Over Time-varying Network

Autori: Hongbing Zhou Yinghui Wang Yiguang Hong

Zdroj: 2018 IEEE 14th International Conference on Control and Automation (ICCA). :716-721

Predmety: 0209 industrial biotechnology, 02 engineering and technology

Prístupová URL adresa: https://dblp.uni-trier.de/db/conf/icca/icca2018.html#WangZH18
https://ieeexplore.ieee.org/document/8444276

On Modification of an Adaptive Stochastic Mirror Descent Algorithm for Convex Optimization Problems with Functional Constraints

Autori: Mohammad Alkousa

Zdroj: Forum for Interdisciplinary Mathematics ISBN: 9789811584978

Predmety: Optimization and Control (math.OC), 0211 other engineering and technologies, FOS: Mathematics, 02 engineering and technology, Mathematics - Optimization and Control

Prístupová URL adresa: http://arxiv.org/pdf/1904.09513
http://arxiv.org/abs/1904.09513
https://arxiv.org/abs/1904.09513
https://ui.adsabs.harvard.edu/abs/2019arXiv190409513A/abstract
http://arxiv.org/pdf/1904.09513.pdf
https://link.springer.com/chapter/10.1007/978-981-15-8498-5_3

Stochastic Mirror Descent Algorithm for L1-Regularized Risk Minimizations

Autori: Hua Ouyang Alexander G. Gray

Zdroj: 2010 10th IEEE International Conference on Computer and Information Technology. :1241-1245

Predmety: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: https://dblp.uni-trier.de/db/conf/IEEEcit/IEEEcit2010.html#OuyangG10
http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000005577880
https://ieeexplore.ieee.org/document/5577880/
https://www.computer.org/csdl/proceedings/cit/2010/4108/00/4108b241.pdf
http://dblp.uni-trier.de/db/conf/IEEEcit/IEEEcit2010.html#OuyangG10

A stochastic mirror-descent algorithm for solving $AXB=C$ over an multi-agent system

Autori: Wang, Yinghui Cheng, Songsong

Predmety: keyword:distributed computation of matrix equation, keyword:multi-agent system, keyword:sublinear convergence, keyword:stochastic mirror descent algorithm, msc:68M15, msc:93A14

Popis súboru: application/pdf

Relation: mr:MR4273575; zbl:Zbl 07396266; reference:[1] Bregman, L. M.: The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming.USSR Computational Mathematics and Mathematical Physics 7 (1967), 200-217. MR 0215617; reference:[2] Chen, G., Zeng, X., Hong, Y.: Distributed optimisation design for solving the Stein equation with constraints.IET Control Theory Appl. 13 (2019), 2492-2499.; reference:[3] Cheng, S., Liang, S.: Distributed optimization for multi-agent system over unbalanced graphs with linear convergence rate.Kybernetika 56 (2020), 559-577. MR 4131743; reference:[4] Deng, W., Zeng, X., Hong, Y.: Distributed computation for solving the Sylvester equation based on optimization.IEEE Control Systems Lett. 4 (2019), 414-419. MR 4211320; reference:[5] Gholami, M. R., Jansson, M., al., E. G. Ström et: Diffusion estimation over cooperative multi-agent networks with missing data.IEEE Trans. Signal Inform. Process. over Networks 2 (2016), 27-289. MR 3571397; reference:[6] Lan, G., Lee, S., Zhou, Y.: Communication-efficient algorithms for decentralized and stochastic optimization.Math. Programm. 180 (2020), 237-284. MR 4062837; reference:[7] Lei, J., Shanbhag, U. V., al., J. S. Pang et: On synchronous, asynchronous, and randomized best-response schemes for stochastic Nash games.Math. Oper. Res. 45 (2020), 157-190. MR 4066993; reference:[8] Liu, J., Morse, A. S., Nedic, A., a., et: Exponential convergence of a distributed algorithm for solving linear algebraic equations.Automatica 83 (2017), 37-46. MR 3680412; reference:[9] Mou, S., Liu, J., Morse, A. S.: A distributed algorithm for solving a linear algebraic equation.IEEE Trans. Automat. Control 60 (2015), 2863-2878. MR 3419577; reference:[10] Ram, S. S., Nedic, A., Veeravalli, V. V.: Distributed stochastic subgradient projection algorithms for convex optimization.J. Optim. Theory Appl. 147 (2010), 516-545. MR 2733992; reference:[11] Shi, G., Anderson, B. D. O., Helmke, U.: Network flows that solve linear equations.IEEE Trans. Automat. Control 62 (2016), 2659-2674. MR 3660554; reference:[12] Wang, Y., Lin, P., Hong, Y.: Distributed regression estimation with incomplete data in multi-agent networks.Science China Inform. Sci. 61 (2018), 092202. MR 3742944, 10.1007/s11432-016-9173-8; reference:[13] Wang, Y., Lin, P., Qin, H.: Distributed classification learning based on nonlinear vector support machines for switching networks.Kybernetika 53 (2017), 595-611. MR 3730254; reference:[14] Wang, Y., Zhao, W., al., Y. Hong et: Distributed subgradient-free stochastic optimization algorithm for nonsmooth convex functions over time-varying networks.SIAM J. Control Optim. 57 (2019), 2821-2842. MR 3995027; reference:[15] Yuan, D., Hong, Y., al., D. W. C. Ho et: Optimal distributed stochastic mirror descent for strongly convex optimization.Automatica 90(2018), 196-203. MR 3764399; reference:[16] Yuan, D., Hong, Y., al., D. W. C. Ho et: Distributed mirror descent for online composite optimization.IEEE Trans. Automat. Control (2020). MR 4210454; reference:[17] Zeng, X., Liang, S., al., Y. Hong et: Distributed computation of linear matrix equations: An optimization perspective.IEEE Trans. Automat. Control 64 (2018), 1858-1873. MR 3951032

Dostupnosť: http://hdl.handle.net/10338.dmlcz/149038

A STOCHASTIC MIRROR-DESCENT ALGORITHM FOR SOLVING AXB = C OVER AN MULTI-AGENT SYSTEM.

Autori: YINGHUI WANG SONGSONG CHENG

Zdroj: Kybernetika; 2021, Vol. 57 Issue 2, p256-271, 16p

Predmety: MULTIAGENT systems, ALGORITHMS, DISTRIBUTED algorithms

Distributed stochastic mirror descent algorithm for resource allocation problem.

Autori: Wang, Yinghui Tu, Zhipeng Qin, Huashu

Zdroj: Control Theory & Technology; Nov2020, Vol. 18 Issue 4, p339-347, 9p

Inertial accelerated stochastic mirror descent for large-scale generalized tensor CP decomposition: Inertial accelerated stochastic mirror descent for large-scale...: Z. Liu et al.

Autori: Liu, Zehui Wang, Qingsong Cui, Chunfeng a ďalší

Zdroj: Computational Optimization & Applications; May2025, Vol. 91 Issue 1, p201-233, 33p

Predmety: LIPSCHITZ continuity, INTEGERS, ALGORITHMS

Sparse recovery by reduced variance stochastic approximation.

Autori: Juditsky, Anatoli Kulunchakov, Andrei Tsyntseus, Hlib

Zdroj: Information & Inference: A Journal of the IMA; Jun2023, Vol. 12 Issue 2, p851-896, 46p

Predmety: STOCHASTIC approximation, ORTHOGONAL matching pursuit, NOISE

Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures.

Autori: Guigues, Vincent

Zdroj: Mathematical Programming; May2017, Vol. 163 Issue 1/2, p169-212, 44p

Predmety: STOCHASTIC analysis, MATHEMATICAL analysis, STOCHASTIC processes, COUNTERPARTY risk, ALGORITHMS

Sequential Sample Average Majorization–Minimization.

Autori: Fort, Gersende Forbes, Florence Nguyen, Hien Duy

Zdroj: Statistics & Computing; Feb2026, Vol. 36 Issue 1, p1-28, 28p

"Relative Continuity" for Non-Lipschitz Nonsmooth Convex Optimization Using Stochastic (or Deterministic) Mirror Descent.

Autori: Lu, Haihao

Zdroj: INFORMS Journal on Optimization; Fall2019, Vol. 1 Issue 4, p288-303, 16p

Predmety: CONVEX functions, NONSMOOTH optimization, SUPPORT vector machines, PROBLEM solving, ALGORITHMS

Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms.

Autori: Zattoni Scroccaro, Pedro Atasoy, Bilge Mohajerin Esfahani, Peyman

Zdroj: Operations Research; Sep/Oct2025, Vol. 73 Issue 5, p2661-2679, 19p

Predmety: ALGORITHMS, MATHEMATICAL optimization, LOSS functions (Statistics), NUMERICAL analysis, MATHEMATICAL programming

Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance

Autori: Vural, Nuri Mert Yu, Lu Balasubramanian, Krishnakumar a ďalší

Témy: Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Optimization and Control, text

URL: http://arxiv.org/abs/2202.11632

A CENTRAL LIMIT THEOREM AND HYPOTHESES TESTING FOR RISK-AVERSE STOCHASTIC PROGRAMS.

Autori: GUIGUES, VINCENT KRÄTSCHMER, VOLKER SHAPIRO, ALEXANDER

Zdroj: SIAM Journal on Optimization; 2018, Vol. 28 Issue 2, p1337-1366, 30p

Predmety: CENTRAL limit theorem, COMPUTER simulation, STOCHASTIC programming, RISK aversion, STATISTICAL hypothesis testing