Literatura científica selecionada sobre o tema "Randomized iterative methods"
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Artigos de revistas sobre o assunto "Randomized iterative methods"
Gower, Robert M., e Peter Richtárik. "Randomized Iterative Methods for Linear Systems". SIAM Journal on Matrix Analysis and Applications 36, n.º 4 (janeiro de 2015): 1660–90. http://dx.doi.org/10.1137/15m1025487.
Texto completo da fonteLoizou, Nicolas, e Peter Richtárik. "Convergence Analysis of Inexact Randomized Iterative Methods". SIAM Journal on Scientific Computing 42, n.º 6 (janeiro de 2020): A3979—A4016. http://dx.doi.org/10.1137/19m125248x.
Texto completo da fonteXing, Lili, Wendi Bao, Ying Lv, Zhiwei Guo e Weiguo Li. "Randomized Block Kaczmarz Methods for Inner Inverses of a Matrix". Mathematics 12, n.º 3 (2 de fevereiro de 2024): 475. http://dx.doi.org/10.3390/math12030475.
Texto completo da fonteZhao, Jing, Xiang Wang e Jianhua Zhang. "Randomized average block iterative methods for solving factorised linear systems". Filomat 37, n.º 14 (2023): 4603–20. http://dx.doi.org/10.2298/fil2314603z.
Texto completo da fonteZhang, Yanjun, e Hanyu Li. "Splitting-based randomized iterative methods for solving indefinite least squares problem". Applied Mathematics and Computation 446 (junho de 2023): 127892. http://dx.doi.org/10.1016/j.amc.2023.127892.
Texto completo da fonteYunak, O., M. Klymash, O. Shpur e V. Mrak. "MATHEMATICAL MODEL OF FRACTAL STRUCTURES RECOGNITION USING NEURAL NETWORK TECHNOLOGY". Information and communication technologies, electronic engineering 3, n.º 1 (junho de 2023): 1–9. http://dx.doi.org/10.23939/ictee2023.01.001.
Texto completo da fonteSabelfeld, Karl K. "Randomized Monte Carlo algorithms for matrix iterations and solving large systems of linear equations". Monte Carlo Methods and Applications 28, n.º 2 (31 de maio de 2022): 125–33. http://dx.doi.org/10.1515/mcma-2022-2114.
Texto completo da fontePopkov, Yuri S., Yuri A. Dubnov e Alexey Yu Popkov. "Reinforcement Procedure for Randomized Machine Learning". Mathematics 11, n.º 17 (23 de agosto de 2023): 3651. http://dx.doi.org/10.3390/math11173651.
Texto completo da fonteXing, Lili, Wendi Bao e Weiguo Li. "On the Convergence of the Randomized Block Kaczmarz Algorithm for Solving a Matrix Equation". Mathematics 11, n.º 21 (5 de novembro de 2023): 4554. http://dx.doi.org/10.3390/math11214554.
Texto completo da fonteShcherbakova, Elena M., Sergey A. Matveev, Alexander P. Smirnov e Eugene E. Tyrtyshnikov. "Study of performance of low-rank nonnegative tensor factorization methods". Russian Journal of Numerical Analysis and Mathematical Modelling 38, n.º 4 (1 de agosto de 2023): 231–39. http://dx.doi.org/10.1515/rnam-2023-0018.
Texto completo da fonteTeses / dissertações sobre o assunto "Randomized iterative methods"
Gower, Robert Mansel. "Sketch and project : randomized iterative methods for linear systems and inverting matrices". Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20989.
Texto completo da fonteBai, Xianglan. "Non-Krylov Non-iterative Subspace Methods For Linear Discrete Ill-posed Problems". Kent State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1627042947894919.
Texto completo da fonteUGWU, UGOCHUKWU OBINNA. "Iterative tensor factorization based on Krylov subspace-type methods with applications to image processing". Kent State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1633531487559183.
Texto completo da fonteGazagnadou, Nidham. "Expected smoothness for stochastic variance-reduced methods and sketch-and-project methods for structured linear systems". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT035.
Texto completo da fonteThe considerable increase in the number of data and features complicates the learning phase requiring the minimization of a loss function. Stochastic gradient descent (SGD) and variance reduction variants (SAGA, SVRG, MISO) are widely used to solve this problem. In practice, these methods are accelerated by computing these stochastic gradients on a "mini-batch": a small group of samples randomly drawn.Indeed, recent technological improvements allowing the parallelization of these calculations have generalized the use of mini-batches.In this thesis, we are interested in the study of variants of stochastic gradient algorithms with reduced variance by trying to find the optimal hyperparameters: step and mini-batch size. Our study allows us to give convergence results interpolating between stochastic methods drawing a single sample per iteration and the so-called "full-batch" gradient descent using all samples at each iteration. Our analysis is based on the expected smoothness constant which allows to capture the regularity of the random function whose gradient is calculated.We study another class of optimization algorithms: the "sketch-and-project" methods. These methods can also be applied as soon as the learning problem boils down to solving a linear system. This is the case of ridge regression. We analyze here variants of this method that use different strategies of momentum and acceleration. These methods also depend on the sketching strategy used to compress the information of the system to be solved at each iteration. Finally, we show that these methods can also be extended to numerical analysis problems. Indeed, the extension of sketch-and-project methods to Alternating-Direction Implicit (ADI) methods allows to apply them to large-scale problems, when the so-called "direct" solvers are too slow
Wu, Wei. "Paving the Randomized Gauss-Seidel". Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/scripps_theses/1074.
Texto completo da fonteCapítulos de livros sobre o assunto "Randomized iterative methods"
Azzam, Joy, Benjamin W. Ong e Allan A. Struthers. "Randomized Iterative Methods for Matrix Approximation". In Machine Learning, Optimization, and Data Science, 226–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95470-3_17.
Texto completo da fonteZhao, Xuefang. "A Randomized Iterative Approach for SV Discovery with SVelter". In Methods in Molecular Biology, 169–77. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8666-8_13.
Texto completo da fonteMárquez, Airam Expósito, e Christopher Expósito-Izquierdo. "An Overview of the Last Advances and Applications of Greedy Randomized Adaptive Search Procedure". In Advances in Computational Intelligence and Robotics, 264–84. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2857-9.ch013.
Texto completo da fonteInchausti, Pablo. "The Generalized Linear Model". In Statistical Modeling With R, 189–200. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192859013.003.0008.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Randomized iterative methods"
Ding, Liyong, Enbin Song e Yunmin Zhu. "Accelerate randomized coordinate descent iterative hard thresholding methods for ℓ0 regularized convex problems". In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7553791.
Texto completo da fonteCarr, Steven, Nils Jansen e Ufuk Topcu. "Verifiable RNN-Based Policies for POMDPs Under Temporal Logic Constraints". In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/570.
Texto completo da fonteJahani, Nazanin, Joaquín Ambía, Kristian Fossum, Sergey Alyaev, Erich Suter e Carlos Torres-Verdín. "REAL-TIME ENSEMBLE-BASED WELL-LOG INTERPRETATION FOR GEOSTEERING". In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0105.
Texto completo da fonteWei He, Hongyan Zhang, Liangpei Zhang e Huanfeng Shen. "A noise-adjusted iterative randomized singular value decomposition method for hyperspectral image denoising". In IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6946731.
Texto completo da fonteFeng, Xu, e Wenjian Yu. "A Fast Adaptive Randomized PCA Algorithm". In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/411.
Texto completo da fonteKaushik, Harshal, e Farzad Yousefian. "A Randomized Block Coordinate Iterative Regularized Subgradient Method for High-dimensional Ill-posed Convex Optimization". In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8815256.
Texto completo da fonteBuermann, Jan, e Jie Zhang. "Multi-Robot Adversarial Patrolling Strategies via Lattice Paths". In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/582.
Texto completo da fonteXie, Jiarui, Chonghui Zhang, Lijun Sun e Yaoyao Fiona Zhao. "Fairness- and Uncertainty-Aware Data Generation for Data-Driven Design". In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-114687.
Texto completo da fonteGao, Guohua, Horacio Florez, Sean Jost, Shakir Shaikh, Kefei Wang, Jeroen Vink, Carl Blom, Terence Wells e Fredrik Saaf. "Implementation of Asynchronous Distributed Gauss-Newton Optimization Algorithms for Uncertainty Quantification by Conditioning to Production Data". In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210118-ms.
Texto completo da fontePITZ, EMIL, SEAN ROONEY e KISHORE POCHIRAJU. "MODELING AND CALIBRATION OF UNCERTAINTY IN MATERIAL PROPERTIES OF ADDITIVELY MANUFACTURED COMPOSITES". In Thirty-sixth Technical Conference. Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/asc36/35758.
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