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Regularized randomized iterative algorithms for factorized linear systems

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  • Du, Kui

Abstract

Randomized iterative algorithms for solving the factorized linear system, ABx=b with A∈Rm×ℓ, B∈Rℓ×n, and b∈Rm, have recently been proposed. They take advantage of the factorized form and avoid forming the matrix C=AB explicitly. However, they can only find the minimum norm (least squares) solution. In contrast, the regularized randomized Kaczmarz (RRK) algorithm can find solutions with certain structures from consistent linear systems. In this work, by combining the randomized Kaczmarz algorithm or the randomized Gauss–Seidel algorithm with the RRK algorithm, we propose two new regularized randomized iterative algorithms to find (least squares) solutions with certain structures of ABx=b. We prove linear convergence of the new algorithms. Computed examples are given to illustrate that the new algorithms can find sparse (least squares) solutions of ABx=b and can be better than the existing randomized iterative algorithms for the corresponding full linear system Cx=b with C=AB.

Suggested Citation

  • Du, Kui, 2024. "Regularized randomized iterative algorithms for factorized linear systems," Applied Mathematics and Computation, Elsevier, vol. 466(C).
    • Handle: RePEc:eee:apmaco:v:466:y:2024:i:c:s0096300323006379
    DOI: 10.1016/j.amc.2023.128468
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    References listed on IDEAS

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    1. Zhang, Yanjun & Li, Hanyu, 2023. "Splitting-based randomized iterative methods for solving indefinite least squares problem," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    2. NESTEROV, Yurii, 2012. "Efficiency of coordinate descent methods on huge-scale optimization problems," LIDAM Reprints CORE 2511, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. D. Leventhal & A. S. Lewis, 2010. "Randomized Methods for Linear Constraints: Convergence Rates and Conditioning," Mathematics of Operations Research, INFORMS, vol. 35(3), pages 641-654, August.
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