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Artykuły w czasopismach na temat "Randomized iterative methods"
Gower, Robert M., i Peter Richtárik. "Randomized Iterative Methods for Linear Systems". SIAM Journal on Matrix Analysis and Applications 36, nr 4 (styczeń 2015): 1660–90. http://dx.doi.org/10.1137/15m1025487.
Pełny tekst źródłaLoizou, Nicolas, i Peter Richtárik. "Convergence Analysis of Inexact Randomized Iterative Methods". SIAM Journal on Scientific Computing 42, nr 6 (styczeń 2020): A3979—A4016. http://dx.doi.org/10.1137/19m125248x.
Pełny tekst źródłaXing, Lili, Wendi Bao, Ying Lv, Zhiwei Guo i Weiguo Li. "Randomized Block Kaczmarz Methods for Inner Inverses of a Matrix". Mathematics 12, nr 3 (2.02.2024): 475. http://dx.doi.org/10.3390/math12030475.
Pełny tekst źródłaZhao, Jing, Xiang Wang i Jianhua Zhang. "Randomized average block iterative methods for solving factorised linear systems". Filomat 37, nr 14 (2023): 4603–20. http://dx.doi.org/10.2298/fil2314603z.
Pełny tekst źródłaZhang, Yanjun, i Hanyu Li. "Splitting-based randomized iterative methods for solving indefinite least squares problem". Applied Mathematics and Computation 446 (czerwiec 2023): 127892. http://dx.doi.org/10.1016/j.amc.2023.127892.
Pełny tekst źródłaYunak, O., M. Klymash, O. Shpur i V. Mrak. "MATHEMATICAL MODEL OF FRACTAL STRUCTURES RECOGNITION USING NEURAL NETWORK TECHNOLOGY". Information and communication technologies, electronic engineering 3, nr 1 (czerwiec 2023): 1–9. http://dx.doi.org/10.23939/ictee2023.01.001.
Pełny tekst źródłaSabelfeld, Karl K. "Randomized Monte Carlo algorithms for matrix iterations and solving large systems of linear equations". Monte Carlo Methods and Applications 28, nr 2 (31.05.2022): 125–33. http://dx.doi.org/10.1515/mcma-2022-2114.
Pełny tekst źródłaPopkov, Yuri S., Yuri A. Dubnov i Alexey Yu Popkov. "Reinforcement Procedure for Randomized Machine Learning". Mathematics 11, nr 17 (23.08.2023): 3651. http://dx.doi.org/10.3390/math11173651.
Pełny tekst źródłaXing, Lili, Wendi Bao i Weiguo Li. "On the Convergence of the Randomized Block Kaczmarz Algorithm for Solving a Matrix Equation". Mathematics 11, nr 21 (5.11.2023): 4554. http://dx.doi.org/10.3390/math11214554.
Pełny tekst źródłaShcherbakova, Elena M., Sergey A. Matveev, Alexander P. Smirnov i Eugene E. Tyrtyshnikov. "Study of performance of low-rank nonnegative tensor factorization methods". Russian Journal of Numerical Analysis and Mathematical Modelling 38, nr 4 (1.08.2023): 231–39. http://dx.doi.org/10.1515/rnam-2023-0018.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaBai, 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.
Pełny tekst źródłaUGWU, 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.
Pełny tekst źródłaGazagnadou, 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.
Pełny tekst źródłaThe 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.
Pełny tekst źródłaCzęści książek na temat "Randomized iterative methods"
Azzam, Joy, Benjamin W. Ong i Allan A. Struthers. "Randomized Iterative Methods for Matrix Approximation". W 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.
Pełny tekst źródłaZhao, Xuefang. "A Randomized Iterative Approach for SV Discovery with SVelter". W 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.
Pełny tekst źródłaMárquez, Airam Expósito, i Christopher Expósito-Izquierdo. "An Overview of the Last Advances and Applications of Greedy Randomized Adaptive Search Procedure". W Advances in Computational Intelligence and Robotics, 264–84. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2857-9.ch013.
Pełny tekst źródłaInchausti, Pablo. "The Generalized Linear Model". W Statistical Modeling With R, 189–200. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192859013.003.0008.
Pełny tekst źródłaStreszczenia konferencji na temat "Randomized iterative methods"
Ding, Liyong, Enbin Song i Yunmin Zhu. "Accelerate randomized coordinate descent iterative hard thresholding methods for ℓ0 regularized convex problems". W 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7553791.
Pełny tekst źródłaCarr, Steven, Nils Jansen i Ufuk Topcu. "Verifiable RNN-Based Policies for POMDPs Under Temporal Logic Constraints". W 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.
Pełny tekst źródłaJahani, Nazanin, Joaquín Ambía, Kristian Fossum, Sergey Alyaev, Erich Suter i Carlos Torres-Verdín. "REAL-TIME ENSEMBLE-BASED WELL-LOG INTERPRETATION FOR GEOSTEERING". W 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0105.
Pełny tekst źródłaWei He, Hongyan Zhang, Liangpei Zhang i Huanfeng Shen. "A noise-adjusted iterative randomized singular value decomposition method for hyperspectral image denoising". W IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2014. http://dx.doi.org/10.1109/igarss.2014.6946731.
Pełny tekst źródłaFeng, Xu, i Wenjian Yu. "A Fast Adaptive Randomized PCA Algorithm". W 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.
Pełny tekst źródłaKaushik, Harshal, i Farzad Yousefian. "A Randomized Block Coordinate Iterative Regularized Subgradient Method for High-dimensional Ill-posed Convex Optimization". W 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8815256.
Pełny tekst źródłaBuermann, Jan, i Jie Zhang. "Multi-Robot Adversarial Patrolling Strategies via Lattice Paths". W 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.
Pełny tekst źródłaXie, Jiarui, Chonghui Zhang, Lijun Sun i Yaoyao Fiona Zhao. "Fairness- and Uncertainty-Aware Data Generation for Data-Driven Design". W 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.
Pełny tekst źródłaGao, Guohua, Horacio Florez, Sean Jost, Shakir Shaikh, Kefei Wang, Jeroen Vink, Carl Blom, Terence Wells i Fredrik Saaf. "Implementation of Asynchronous Distributed Gauss-Newton Optimization Algorithms for Uncertainty Quantification by Conditioning to Production Data". W SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210118-ms.
Pełny tekst źródłaPITZ, EMIL, SEAN ROONEY i KISHORE POCHIRAJU. "MODELING AND CALIBRATION OF UNCERTAINTY IN MATERIAL PROPERTIES OF ADDITIVELY MANUFACTURED COMPOSITES". W Thirty-sixth Technical Conference. Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/asc36/35758.
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