Literatura académica sobre el tema "Stochastic Newton algorithms"
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Artículos de revistas sobre el tema "Stochastic Newton algorithms"
Kovacevic, Ivana, Branko Kovacevic y Zeljko Djurovic. "On strong consistency of a class of recursive stochastic Newton-Raphson type algorithms with application to robust linear dynamic system identification". Facta universitatis - series: Electronics and Energetics 21, n.º 1 (2008): 1–21. http://dx.doi.org/10.2298/fuee0801001k.
Texto completoYousefi, Mahsa y Ángeles Martínez. "Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods". Algorithms 16, n.º 10 (20 de octubre de 2023): 490. http://dx.doi.org/10.3390/a16100490.
Texto completoForneron, Jean-Jacques y Serena Ng. "Estimation and Inference by Stochastic Optimization: Three Examples". AEA Papers and Proceedings 111 (1 de mayo de 2021): 626–30. http://dx.doi.org/10.1257/pandp.20211038.
Texto completoCao, Pengfei y Xionglin Luo. "Performance analysis of multi-innovation stochastic Newton recursive algorithms". Digital Signal Processing 56 (septiembre de 2016): 15–23. http://dx.doi.org/10.1016/j.dsp.2016.05.005.
Texto completoGhoshdastidar, Debarghya, Ambedkar Dukkipati y Shalabh Bhatnagar. "Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms". Automatica 50, n.º 10 (octubre de 2014): 2606–14. http://dx.doi.org/10.1016/j.automatica.2014.08.021.
Texto completoShao, Wei y Guangbao Guo. "Multiple-Try Simulated Annealing Algorithm for Global Optimization". Mathematical Problems in Engineering 2018 (17 de julio de 2018): 1–11. http://dx.doi.org/10.1155/2018/9248318.
Texto completoGao, Guohua, Gaoming Li y Albert Coburn Reynolds. "A Stochastic Optimization Algorithm for Automatic History Matching". SPE Journal 12, n.º 02 (1 de junio de 2007): 196–208. http://dx.doi.org/10.2118/90065-pa.
Texto completoWang, Qing y Yang Cao. "Stochastic Finite Element Method for Nonlinear Dynamic Problem with Random Parameters". Advanced Materials Research 189-193 (febrero de 2011): 1348–57. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.1348.
Texto completoWang, Yanshan, In-Chan Choi y Hongfang Liu. "Generalized ensemble model for document ranking in information retrieval". Computer Science and Information Systems 14, n.º 1 (2017): 123–51. http://dx.doi.org/10.2298/csis160229042w.
Texto completoClayton, R. P. y R. F. Martinez-Botas. "Application of generic algorithms in aerodynamic optimisation design procedures". Aeronautical Journal 108, n.º 1090 (diciembre de 2004): 611–20. http://dx.doi.org/10.1017/s0001924000000440.
Texto completoTesis sobre el tema "Stochastic Newton algorithms"
Lu, Wei. "Μéthοdes stοchastiques du secοnd οrdre pοur le traitement séquentiel de dοnnées massives". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR13.
Texto completoWith the rapid development of technologies and the acquisition of big data, methods capable of processing data sequentially (online) have become indispensable. Among these methods, stochastic gradient algorithms have been established for estimating the minimizer of a function expressed as the expectation of a random function. Although they have become essential, these algorithms encounter difficulties when the problem is ill-conditioned. In this thesis, we focus on second-order stochastic algorithms, such as those of the Newton type, and their applications to various statistical and optimization problems. After establishing theoretical foundations and exposing the motivations that lead us to explore stochastic Newton algorithms, we develop the various contributions of this thesis. The first contribution concerns the study and development of stochastic Newton algorithms for ridge linear regression and ridge logistic regression. These algorithms are based on the Riccati formula (Sherman-Morrison) to recursively estimate the inverse of the Hessian. As the acquisition of big data is generally accompanied by a contamination of the latter, in a second contribution, we focus on the online estimation of the geometric median, which is a robust indicator, i.e., not very sensitive to the presence of atypical data. More specifically, we propose a new stochastic Newton estimator to estimate the geometric median. In the first two contributions, the estimators of the Hessians' inverses are constructed using the Riccati formula, but this is only possible for certain functions. Thus, our third contribution introduces a new Robbins-Monro type method for online estimation of the Hessian's inverse, allowing us then to develop universal stochastic Newton algorithms. Finally, our last contribution focuses on Full Adagrad type algorithms, where the difficulty lies in the fact that there is an adaptive step based on the square root of the inverse of the gradient's covariance. We thus propose a Robbins-Monro type algorithm to estimate this matrix, allowing us to propose a recursive approach for Full AdaGrad and its streaming version, with reduced computational costs. For all the new estimators we propose, we establish their convergence rates as well as their asymptotic efficiency. Moreover, we illustrate the efficiency of these algorithms using numerical simulations and by applying them to real data
Stewart, Alistair Mark. "Efficient algorithms for infinite-state recursive stochastic models and Newton's method". Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10001.
Texto completoLakshmanan, K. "Online Learning and Simulation Based Algorithms for Stochastic Optimization". Thesis, 2012. http://etd.iisc.ac.in/handle/2005/3245.
Texto completoLakshmanan, K. "Online Learning and Simulation Based Algorithms for Stochastic Optimization". Thesis, 2012. http://hdl.handle.net/2005/3245.
Texto completoMondal, Akash. "Stochastic Optimization And Its Application In Reinforcement Learning". Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6086.
Texto completoGupta, Saurabh. "Development Of Deterministic And Stochastic Algorithms For Inverse Problems Of Optical Tomography". Thesis, 2013. https://etd.iisc.ac.in/handle/2005/2608.
Texto completoGupta, Saurabh. "Development Of Deterministic And Stochastic Algorithms For Inverse Problems Of Optical Tomography". Thesis, 2013. http://etd.iisc.ernet.in/handle/2005/2608.
Texto completoMartin, James Robert Ph D. "A computational framework for the solution of infinite-dimensional Bayesian statistical inverse problems with application to global seismic inversion". Thesis, 2015. http://hdl.handle.net/2152/31374.
Texto completoCapítulos de libros sobre el tema "Stochastic Newton algorithms"
Bhatnagar, S., H. Prasad y L. Prashanth. "Newton-Based Smoothed Functional Algorithms". En Stochastic Recursive Algorithms for Optimization, 133–48. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_8.
Texto completoBhatnagar, S., H. Prasad y L. Prashanth. "Newton-Based Simultaneous Perturbation Stochastic Approximation". En Stochastic Recursive Algorithms for Optimization, 105–31. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_7.
Texto completoHe, Sailing, Staffan Strom y Vaughan H. Weston. "Wave-Splittings Combined With Optimization Techniques". En Time Domain Wave-Splittings and Inverse Problems, 185–228. Oxford University PressOxford, 1998. http://dx.doi.org/10.1093/oso/9780198565499.003.0005.
Texto completoArsham, Hossein y Shaya Sheikh. "Organizational Performance-Design Process". En Advances in Business Information Systems and Analytics, 54–84. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7272-7.ch005.
Texto completoJabari, Farkhondeh, Heresh Seyedia, Sajad Najafi Ravadanegh y Behnam Mohammadi Ivatloo. "Stochastic Contingency Analysis Based on Voltage Stability Assessment in Islanded Power System Considering Load Uncertainty Using MCS and k-PEM". En Advances in Computer and Electrical Engineering, 12–36. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9911-3.ch002.
Texto completoActas de conferencias sobre el tema "Stochastic Newton algorithms"
Graillat, Stef, Fabienne Jezequel, Enzo Queiros Martins y Maxime Spyropoulos. "Computing multiple roots of polynomials in stochastic arithmetic with Newton method and approximate GCD". En 2021 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2021. http://dx.doi.org/10.1109/synasc54541.2021.00020.
Texto completoArun, C. O., B. N. Rao y S. M. Sivakumar. "Stochastic Damage Growth Analysis Using EFGM". En ASME 2008 Pressure Vessels and Piping Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/pvp2008-61882.
Texto completoZhang, Shumao, Fahim Forouzanfar y Xiao-Hui Wu. "Stein Variational Gradient Descent for Reservoir History Matching Problems". En SPE Reservoir Simulation Conference. SPE, 2023. http://dx.doi.org/10.2118/212190-ms.
Texto completoEltahan, Esmail, Faruk Omer Alpak y Kamy Sepehrnoori. "A Quasi-Newton Method for Well Location Optimization Under Uncertainty". En SPE Reservoir Simulation Conference. SPE, 2023. http://dx.doi.org/10.2118/212212-ms.
Texto completoFang, X. y J. Tang. "Granular Damping Analysis Using a Direct Simulation Monte Carlo Approach". En ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14448.
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