Literatura académica sobre el tema "Subgradient descent"
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Artículos de revistas sobre el tema "Subgradient descent"
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 completoTesis sobre el tema "Subgradient descent"
Beltran, Royo César. "Generalized unit commitment by the radar multiplier method". Doctoral thesis, Universitat Politècnica de Catalunya, 2001. http://hdl.handle.net/10803/6501.
Texto completoThis thesis report is structured as follows. Chapter 1 describes the state of the art of the UC and GUC problems. The formulation of the classical short-term power planning problems related to the GUC problem, namely the economic dispatching problem, the OPF problem, and the UC problem, are reviewed. Special attention is paid to the UC literature and to the traditional methods for solving the UC problem. In chapter 2 we extend the OPF model developed by professors Heredia and Nabona to obtain our GUC model. The variables used and the modelling of the thermal, hydraulic and transmission systems are introduced, as is the objective function. Chapter 3 deals with the Variable Duplication (VD) method, which is used to decompose the GUC problem as an alternative to the Classical Lagrangian Relaxation (CLR) method. Furthermore, in chapter 3 dual bounds provided by the VDmethod or by the CLR methods are theoretically compared.
Throughout chapters 4, 5, and 6 our solution methodology, the Radar Multiplier (RM) method, is designed and tested. Three independent matters are studied: first, the auxiliary problem principle method, used by Batut and Renaud to treat the inseparable augmented Lagrangian, is compared with the block coordinate descent method from both theoretical and practical points of view. Second, the Radar Sub- gradient (RS) method, a new Lagrange multiplier updating method, is proposed and computationally compared with the classical subgradient method. And third, we study the local character of the optimizers computed by the Augmented Lagrangian Relaxation (ALR) method when solving the GUC problem. A heuristic to improve the local ALR optimizers is designed and tested.
Chapter 7 is devoted to our computational implementation of the RM method, the MACH code. First, the design of MACH is reviewed brie y and then its performance is tested by solving real-life large-scale UC and GUC instances. Solutions computed using our VD formulation of the GUC problem are partially primal feasible since they do not necessarily fulfill the spinning reserve constraints. In chapter 8 we study how to modify this GUC formulation with the aim of obtaining full primal feasible solutions. A successful test based on a simple UC problem is reported. The conclusions, contributions of the thesis, and proposed further research can be found in chapter 9.
Yaji, Vinayaka Ganapati. "Stochastic approximation with set-valued maps and Markov noise: Theoretical foundations and applications". Thesis, 2017. https://etd.iisc.ac.in/handle/2005/5461.
Texto completoCapítulos de libros sobre el tema "Subgradient descent"
Kiwiel, Krzysztof C. "Aggregate subgradient methods for unconstrained convex minimization". En Methods of Descent for Nondifferentiable Optimization, 44–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0074502.
Texto completoKiwiel, Krzysztof C. "Methods with subgradient locality measures for minimizing nonconvex functions". En Methods of Descent for Nondifferentiable Optimization, 87–138. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0074503.
Texto completoKiwiel, Krzysztof C. "Methods with subgradient deletion rules for unconstrained nonconvex minimization". En Methods of Descent for Nondifferentiable Optimization, 139–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/bfb0074504.
Texto completoActas de conferencias sobre el tema "Subgradient descent"
Gez, Tamir L. S. y Kobi Cohen. "Subgradient Descent Learning with Over-the-Air Computation". En ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095134.
Texto completoZhang, Honghui, Jingdong Wang, Ping Tan, Jinglu Wang y Long Quan. "Learning CRFs for Image Parsing with Adaptive Subgradient Descent". En 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.382.
Texto completoLucchi, Aurelien, Yunpeng Li y Pascal Fua. "Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets". En 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.259.
Texto completoSinghal, Manmohan y Saurabh Khanna. "Proximal Subgradient Descent Method for Cancelling Cross-Interference in FMCW Radars". En 2023 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2023. http://dx.doi.org/10.1109/ssp53291.2023.10208039.
Texto completoWu, Songwei, Hang Yu y Justin Dauwels. "Efficient Stochastic Subgradient Descent Algorithms for High-dimensional Semi-sparse Graphical Model Selection". En ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683823.
Texto completoWan, Yuanyu, Nan Wei y Lijun Zhang. "Efficient Adaptive Online Learning via Frequent Directions". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/381.
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