Добірка наукової літератури з теми "Randomized iterative methods"
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Статті в журналах з теми "Randomized iterative methods"
Gower, Robert M., and Peter Richtárik. "Randomized Iterative Methods for Linear Systems." SIAM Journal on Matrix Analysis and Applications 36, no. 4 (January 2015): 1660–90. http://dx.doi.org/10.1137/15m1025487.
Повний текст джерелаLoizou, Nicolas, and Peter Richtárik. "Convergence Analysis of Inexact Randomized Iterative Methods." SIAM Journal on Scientific Computing 42, no. 6 (January 2020): A3979—A4016. http://dx.doi.org/10.1137/19m125248x.
Повний текст джерелаXing, Lili, Wendi Bao, Ying Lv, Zhiwei Guo, and Weiguo Li. "Randomized Block Kaczmarz Methods for Inner Inverses of a Matrix." Mathematics 12, no. 3 (February 2, 2024): 475. http://dx.doi.org/10.3390/math12030475.
Повний текст джерелаZhao, Jing, Xiang Wang, and Jianhua Zhang. "Randomized average block iterative methods for solving factorised linear systems." Filomat 37, no. 14 (2023): 4603–20. http://dx.doi.org/10.2298/fil2314603z.
Повний текст джерелаZhang, Yanjun, and Hanyu Li. "Splitting-based randomized iterative methods for solving indefinite least squares problem." Applied Mathematics and Computation 446 (June 2023): 127892. http://dx.doi.org/10.1016/j.amc.2023.127892.
Повний текст джерелаYunak, O., M. Klymash, O. Shpur, and V. Mrak. "MATHEMATICAL MODEL OF FRACTAL STRUCTURES RECOGNITION USING NEURAL NETWORK TECHNOLOGY." Information and communication technologies, electronic engineering 3, no. 1 (June 2023): 1–9. http://dx.doi.org/10.23939/ictee2023.01.001.
Повний текст джерелаSabelfeld, Karl K. "Randomized Monte Carlo algorithms for matrix iterations and solving large systems of linear equations." Monte Carlo Methods and Applications 28, no. 2 (May 31, 2022): 125–33. http://dx.doi.org/10.1515/mcma-2022-2114.
Повний текст джерелаPopkov, Yuri S., Yuri A. Dubnov, and Alexey Yu Popkov. "Reinforcement Procedure for Randomized Machine Learning." Mathematics 11, no. 17 (August 23, 2023): 3651. http://dx.doi.org/10.3390/math11173651.
Повний текст джерелаXing, Lili, Wendi Bao, and Weiguo Li. "On the Convergence of the Randomized Block Kaczmarz Algorithm for Solving a Matrix Equation." Mathematics 11, no. 21 (November 5, 2023): 4554. http://dx.doi.org/10.3390/math11214554.
Повний текст джерелаShcherbakova, Elena M., Sergey A. Matveev, Alexander P. Smirnov, and Eugene E. Tyrtyshnikov. "Study of performance of low-rank nonnegative tensor factorization methods." Russian Journal of Numerical Analysis and Mathematical Modelling 38, no. 4 (August 1, 2023): 231–39. http://dx.doi.org/10.1515/rnam-2023-0018.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаBai, 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.
Повний текст джерелаUGWU, 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.
Повний текст джерелаGazagnadou, 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.
Повний текст джерелаThe 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.
Повний текст джерелаЧастини книг з теми "Randomized iterative methods"
Azzam, Joy, Benjamin W. Ong, and 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.
Повний текст джерелаZhao, 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.
Повний текст джерелаMárquez, Airam Expósito, and 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.
Повний текст джерелаInchausti, 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.
Повний текст джерелаТези доповідей конференцій з теми "Randomized iterative methods"
Ding, Liyong, Enbin Song та Yunmin Zhu. "Accelerate randomized coordinate descent iterative hard thresholding methods for ℓ0 regularized convex problems". У 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7553791.
Повний текст джерелаCarr, Steven, Nils Jansen, and 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.
Повний текст джерелаJahani, Nazanin, Joaquín Ambía, Kristian Fossum, Sergey Alyaev, Erich Suter, and 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.
Повний текст джерелаWei He, Hongyan Zhang, Liangpei Zhang, and 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.
Повний текст джерелаFeng, Xu, and 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.
Повний текст джерелаKaushik, Harshal, and 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.
Повний текст джерелаBuermann, Jan, and 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.
Повний текст джерелаXie, Jiarui, Chonghui Zhang, Lijun Sun, and 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.
Повний текст джерелаGao, Guohua, Horacio Florez, Sean Jost, Shakir Shaikh, Kefei Wang, Jeroen Vink, Carl Blom, Terence Wells, and 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.
Повний текст джерелаPITZ, EMIL, SEAN ROONEY, and 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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