Academic literature on the topic 'Randomized iterative methods'
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Journal articles on the topic "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.
Loizou, 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.
Xing, 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.
Zhao, 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.
Zhang, 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.
Yunak, 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.
Sabelfeld, 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.
Popkov, 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.
Xing, 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.
Shcherbakova, 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.
Dissertations / Theses on the topic "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.
Bai, 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.
UGWU, 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.
Gazagnadou, 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.
The 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.
Book chapters on the topic "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.
Zhao, 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.
Márquez, 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.
Inchausti, 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.
Conference papers on the topic "Randomized iterative methods":
Ding, Liyong, Enbin Song, and 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.
Carr, 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.
Jahani, 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.
Wei 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.
Feng, 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.
Kaushik, 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.
Buermann, 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.
Xie, 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.
Gao, 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.
PITZ, 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.