Artículos de revistas sobre el tema "Subgradient descent"
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Krutikov, Vladimir, Svetlana Gutova, Elena Tovbis, Lev Kazakovtsev y Eugene Semenkin. "Relaxation Subgradient Algorithms with Machine Learning Procedures". Mathematics 10, n.º 21 (25 de octubre de 2022): 3959. http://dx.doi.org/10.3390/math10213959.
Texto completoTovbis, Elena, Vladimir Krutikov, Predrag Stanimirović, Vladimir Meshechkin, Aleksey Popov y Lev Kazakovtsev. "A Family of Multi-Step Subgradient Minimization Methods". Mathematics 11, n.º 10 (11 de mayo de 2023): 2264. http://dx.doi.org/10.3390/math11102264.
Texto completoLi, Gang, Minghua Li y Yaohua Hu. "Stochastic quasi-subgradient method for stochastic quasi-convex feasibility problems". Discrete & Continuous Dynamical Systems - S 15, n.º 4 (2022): 713. http://dx.doi.org/10.3934/dcdss.2021127.
Texto completoChu, Wenqing, Yao Hu, Chen Zhao, Haifeng Liu y Deng Cai. "Atom Decomposition Based Subgradient Descent for matrix classification". Neurocomputing 205 (septiembre de 2016): 222–28. http://dx.doi.org/10.1016/j.neucom.2016.03.069.
Texto completoBedi, Amrit Singh y Ketan Rajawat. "Network Resource Allocation via Stochastic Subgradient Descent: Convergence Rate". IEEE Transactions on Communications 66, n.º 5 (mayo de 2018): 2107–21. http://dx.doi.org/10.1109/tcomm.2018.2792430.
Texto completoNedić, Angelia y Soomin Lee. "On Stochastic Subgradient Mirror-Descent Algorithm with Weighted Averaging". SIAM Journal on Optimization 24, n.º 1 (enero de 2014): 84–107. http://dx.doi.org/10.1137/120894464.
Texto completoCui, Yun-Ling, Lu-Chuan Ceng, Fang-Fei Zhang, Cong-Shan Wang, Jian-Ye Li, Hui-Ying Hu y Long He. "Modified Mann-Type Subgradient Extragradient Rules for Variational Inequalities and Common Fixed Points Implicating Countably Many Nonexpansive Operators". Mathematics 10, n.º 11 (6 de junio de 2022): 1949. http://dx.doi.org/10.3390/math10111949.
Texto completoMontonen, O., N. Karmitsa y M. M. Mäkelä. "Multiple subgradient descent bundle method for convex nonsmooth multiobjective optimization". Optimization 67, n.º 1 (12 de octubre de 2017): 139–58. http://dx.doi.org/10.1080/02331934.2017.1387259.
Texto completoBeck, Amir y Marc Teboulle. "Mirror descent and nonlinear projected subgradient methods for convex optimization". Operations Research Letters 31, n.º 3 (mayo de 2003): 167–75. http://dx.doi.org/10.1016/s0167-6377(02)00231-6.
Texto completoCeng, Lu-Chuan, Li-Jun Zhu y Tzu-Chien Yin. "Modified subgradient extragradient algorithms for systems of generalized equilibria with constraints". AIMS Mathematics 8, n.º 2 (2023): 2961–94. http://dx.doi.org/10.3934/math.2023154.
Texto completoAuslender, A. y M. Teboulle. "Interior Gradient and Epsilon-Subgradient Descent Methods for Constrained Convex Minimization". Mathematics of Operations Research 29, n.º 1 (febrero de 2004): 1–26. http://dx.doi.org/10.1287/moor.1030.0062.
Texto completoCui, Yun-Ling, Lu-Chuan Ceng, Fang-Fei Zhang, Liang He, Jie Yin, Cong-Shan Wang y Hui-Ying Hu. "Mann Hybrid Deepest-Descent Extragradient Method with Line-Search Process for Hierarchical Variational Inequalities for Countable Nonexpansive Mappings". Journal of Mathematics 2023 (15 de mayo de 2023): 1–18. http://dx.doi.org/10.1155/2023/6177912.
Texto completoPanup, Wanida y Rabian Wangkeeree. "Stochastic Subgradient for Large-Scale Support Vector Machine Using the Generalized Pinball Loss Function". Symmetry 13, n.º 9 (8 de septiembre de 2021): 1652. http://dx.doi.org/10.3390/sym13091652.
Texto completoBoţ, Radu Ioan y Axel Böhm. "An incremental mirror descent subgradient algorithm with random sweeping and proximal step". Optimization 68, n.º 1 (14 de junio de 2018): 33–50. http://dx.doi.org/10.1080/02331934.2018.1482491.
Texto completoGokbayrak, Kagan y Omer Selvi. "A Subgradient Descent Algorithm for Optimization of Initially Controllable Flow Shop Systems". Discrete Event Dynamic Systems 19, n.º 2 (4 de febrero de 2009): 267–82. http://dx.doi.org/10.1007/s10626-009-0061-z.
Texto completoArachie, Chidubem y Bert Huang. "Adversarial Label Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 3183–90. http://dx.doi.org/10.1609/aaai.v33i01.33013183.
Texto completoGebken, Bennet y Sebastian Peitz. "An Efficient Descent Method for Locally Lipschitz Multiobjective Optimization Problems". Journal of Optimization Theory and Applications 188, n.º 3 (13 de enero de 2021): 696–723. http://dx.doi.org/10.1007/s10957-020-01803-w.
Texto completoStonyakin, Fedor Sergeevich, Aleksej N. Stepanov, Alexander Vladimirovich Gasnikov y Alexander A. Titov. "Mirror descent for constrained optimization problems with large subgradient values of functional constraints". Computer Research and Modeling 12, n.º 2 (abril de 2020): 301–17. http://dx.doi.org/10.20537/2076-7633-2020-12-2-301-317.
Texto completoKorablev, A. I. y V. V. Eisman. "A general method of finding the direction of descent in ε-subgradient methods". Journal of Mathematical Sciences 74, n.º 6 (mayo de 1995): 1327–31. http://dx.doi.org/10.1007/bf02367719.
Texto completoRobinson, Stephen M. "Linear convergence of epsilon-subgradient descent methods for a class of convex functions". Mathematical Programming 86, n.º 1 (1 de septiembre de 1999): 41–50. http://dx.doi.org/10.1007/s101070050078.
Texto completoWang, Ximing, Neng Fan y Panos M. Pardalos. "Stochastic subgradient descent method for large-scale robust chance-constrained support vector machines". Optimization Letters 11, n.º 5 (15 de marzo de 2016): 1013–24. http://dx.doi.org/10.1007/s11590-016-1026-4.
Texto completoHand, Paul, Oscar Leong y Vladislav Voroninski. "Optimal sample complexity of subgradient descent for amplitude flow via non-Lipschitz matrix concentration". Communications in Mathematical Sciences 19, n.º 7 (2021): 2035–47. http://dx.doi.org/10.4310/cms.2021.v19.n7.a11.
Texto completoLi, Liping, Wei Xu, Tianyi Chen, Georgios B. Giannakis y Qing Ling. "RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 1544–51. http://dx.doi.org/10.1609/aaai.v33i01.33011544.
Texto completoCeng, Lu-Chuan, Ching-Feng Wen, Yeong-Cheng Liou y Jen-Chih Yao. "On Strengthened Inertial-Type Subgradient Extragradient Rule with Adaptive Step Sizes for Variational Inequalities and Fixed Points of Asymptotically Nonexpansive Mappings". Mathematics 10, n.º 6 (17 de marzo de 2022): 958. http://dx.doi.org/10.3390/math10060958.
Texto completoCeng, Lu-Chuan, Xiaolong Qin, Yekini Shehu y Jen-Chih Yao. "Mildly Inertial Subgradient Extragradient Method for Variational Inequalities Involving an Asymptotically Nonexpansive and Finitely Many Nonexpansive Mappings". Mathematics 7, n.º 10 (22 de septiembre de 2019): 881. http://dx.doi.org/10.3390/math7100881.
Texto completoLuo, Songting, Shingyu Leung y Jianliang Qian. "An Adjoint State Method for Numerical Approximation of Continuous Traffic Congestion Equilibria". Communications in Computational Physics 10, n.º 5 (noviembre de 2011): 1113–31. http://dx.doi.org/10.4208/cicp.020210.311210a.
Texto completoXu, Hang, Song Li y Junhong Lin. "Low rank matrix recovery with adversarial sparse noise*". Inverse Problems 38, n.º 3 (18 de enero de 2022): 035001. http://dx.doi.org/10.1088/1361-6420/ac44dc.
Texto completoПеревозчиков, Александр Геннадьевич, Валерий Юрьевич Решетов y Александра Ильинична Лесик. "The ''attack-defense'' model on networks with the initial residuals of the parties". Herald of Tver State University. Series: Applied Mathematics, n.º 2 (21 de julio de 2021): 68–81. http://dx.doi.org/10.26456/vtpmk618.
Texto completoChen, Lin, Ji-Ting Jia, Qiong Zhang, Wan-Yu Deng y Wei Wei. "Online Sequential Projection Vector Machine with Adaptive Data Mean Update". Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5197932.
Texto completoBaillon, J. B. y R. Cominetti. "A Convergence Result for Nonautonomous Subgradient Evolution Equations and Its Application to the Steepest Descent Exponential Penalty Trajectory in Linear Programming". Journal of Functional Analysis 187, n.º 2 (diciembre de 2001): 263–73. http://dx.doi.org/10.1006/jfan.2001.3828.
Texto completoПеревозчиков, Александр Геннадьевич, Валерий Юрьевич Решетов y Александра Ильинична Лесик. "The ``attack-defense'' game with restrictions on the intake capacity of points". Herald of Tver State University. Series: Applied Mathematics, n.º 3 (30 de noviembre de 2020): 78–92. http://dx.doi.org/10.26456/vtpmk600.
Texto completoXia, Zun-quan. "Finding subgradients or descent directions of convex functions by external polyhedral approximation of subdifferentials". Optimization Methods and Software 1, n.º 3 (enero de 1992): 253–64. http://dx.doi.org/10.1080/10556789208805523.
Texto completoGriewank, Andreas y Andrea Walther. "Polyhedral DC Decomposition and DCA Optimization of Piecewise Linear Functions". Algorithms 13, n.º 7 (11 de julio de 2020): 166. http://dx.doi.org/10.3390/a13070166.
Texto completoDick, Josef, Guoyin Li y Dinh Duy Tran. "A new regularization for sparse optimization". ANZIAM Journal 62 (7 de febrero de 2022): C176—C191. http://dx.doi.org/10.21914/anziamj.v62.16076.
Texto completoGu, Bin, Yingying Shan, Xin Quan y Guansheng Zheng. "Accelerating Sequential Minimal Optimization via Stochastic Subgradient Descent". IEEE Transactions on Cybernetics, 2019, 1–9. http://dx.doi.org/10.1109/tcyb.2019.2893289.
Texto completoSchechtman, S. "Stochastic proximal subgradient descent oscillates in the vicinity of its accumulation set". Optimization Letters, 6 de mayo de 2022. http://dx.doi.org/10.1007/s11590-022-01884-8.
Texto completoKrygin, V. y R. Khomenko. "Self-Driven Algorithm for Solving Supermodular (max,+) Labeling Problems Based on Subgradient Descent*". Cybernetics and Systems Analysis, 21 de octubre de 2022. http://dx.doi.org/10.1007/s10559-022-00485-8.
Texto completoGez, Tamir L. S. y Kobi Cohen. "Subgradient Descent Learning over Fading Multiple Access Channels with Over-the-Air Computation". IEEE Access, 2023, 1. http://dx.doi.org/10.1109/access.2023.3291023.
Texto completoCeng, Lu-Chuan y Qing Yuan. "Composite inertial subgradient extragradient methods for variational inequalities and fixed point problems". Journal of Inequalities and Applications 2019, n.º 1 (26 de octubre de 2019). http://dx.doi.org/10.1186/s13660-019-2229-x.
Texto completoBoţ, Radu Ioan, Minh N. Dao y Guoyin Li. "Extrapolated Proximal Subgradient Algorithms for Nonconvex and Nonsmooth Fractional Programs". Mathematics of Operations Research, 7 de diciembre de 2021. http://dx.doi.org/10.1287/moor.2021.1214.
Texto completoChen, Jia y Ioannis D. Schizas. "Multimodal correlations-based data clustering". Foundations of Data Science, 2022, 0. http://dx.doi.org/10.3934/fods.2022011.
Texto completoLatz, Jonas. "Gradient flows and randomised thresholding: sparse inversion and classification". Inverse Problems, 19 de octubre de 2022. http://dx.doi.org/10.1088/1361-6420/ac9b84.
Texto completoJianhong, Wang y Ricardo A. Ramirez-Mendoza. "Synthesis cascade estimation for aircraft system identification". Aircraft Engineering and Aerospace Technology, 13 de junio de 2022. http://dx.doi.org/10.1108/aeat-03-2022-0093.
Texto completoDutta, Haimonti. "A Consensus Algorithm for Linear Support Vector Machines". Management Science, 30 de agosto de 2021. http://dx.doi.org/10.1287/mnsc.2021.4042.
Texto completoWen, Jiajun, Wai Keung Wong, Xiao-Li Hu, Honglin Chu y Zhihui Lai. "Restricted subgradient descend method for sparse signal learning". International Journal of Machine Learning and Cybernetics, 26 de abril de 2022. http://dx.doi.org/10.1007/s13042-022-01551-5.
Texto completoWen, Jiajun, Wai Keung Wong, Xiao-Li Hu, Honglin Chu y Zhihui Lai. "Restricted subgradient descend method for sparse signal learning". International Journal of Machine Learning and Cybernetics, 26 de abril de 2022. http://dx.doi.org/10.1007/s13042-022-01551-5.
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