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The randomized circumcentered-reflection iteration method for solving consistent linear equations

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Abstract

A novel iteration scheme for solving consistent linear equations is introduced by exploring the circumcenter of a n-dimensional triangle formed with a starting point and its reflections across two hyperplanes. The proposed method, akin to the block Kaczmarz method, provides optimal linear combination within two subspaces. The convergence rate in expectation is also addressed in detail if the row is selected with probability proportional to p-norm of residuals. Numerical experiments demonstrate its superiority over randomized Kaczmarz and reflection methods, particularly evident as highly coherent rows. Performance are enhanced by proper parameter-tuning of p.

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References

  • Ansorge R (1984) Connections between the Cimmino-method and the Kaczmarz-method for the solution of singular and regular systems of equations. Computing 33(3):367–375

    Article  MathSciNet  Google Scholar 

  • Bai Z-Z, Wen-Ting W (2018) On greedy randomized Kaczmarz method for solving large sparse linear systems. SIAM J Sci Comput 40(1):A592–A606

    Article  MathSciNet  Google Scholar 

  • Behling R, Cruz JYB, Santos L-R (2018) Circumcentering the Douglas–Rachford method. Numer Algorithms 78(3):759–776

    Article  MathSciNet  Google Scholar 

  • Cimmino G (1938) Cacolo approssimato per le soluzioni dei systemi di equazioni lineari. La Ricerca Scientifica (Roma) 1:326–333

    Google Scholar 

  • De Loera JA, Haddock J, Needell D (2017) A sampling Kaczmarz–Motzkin algorithm for linear feasibility. SIAM J Sci Comput 39(5):S66–S87

    Article  MathSciNet  Google Scholar 

  • Du Y-S, Hayami K, Zheng N, Morikuni K, Yin J-F (2021) Kaczmarz-type inner-iteration preconditioned flexible GMRES methods for consistent linear systems. SIAM J Sci Comput 43(5):S345–S366

    Article  MathSciNet  Google Scholar 

  • Duff IS, Guivarch R, Ruiz D, Zenadi M (2015) The augmented block Cimmino distributed method. SIAM J Sci Comput 37(3):A1248–A1269

    Article  MathSciNet  Google Scholar 

  • Filipović D, Glau K, Nakatsukasa Y, Statti F (2019) Weighted Monte Carlo with least squares and randomized extended Kaczmarz for option pricing. Swiss Finance Institute Research Paper (19-54)

  • Gordon R, Bender R, Herman GT (1970) Algebraic reconstruction techniques (art) for three-dimensional electron microscopy and x-ray photography. J Theor Biol 29(3):471–481

    Article  Google Scholar 

  • Herman GT, Davidi R (2008) Image reconstruction from a small number of projections. Inverse Problems 24(4):045011

    Article  MathSciNet  Google Scholar 

  • Iusem AN, De Pierro AR (1986) Convergence results for an accelerated nonlinear Cimmino algorithm. Numer Math 49(4):367–378

    Article  MathSciNet  Google Scholar 

  • Jiang X-L, Zhang K, Yin J-F (2022) Randomized block Kaczmarz methods with k-means clustering for solving large linear systems. J Comput Appl Math 403:113828

    Article  MathSciNet  Google Scholar 

  • Karczmarz S (1937) Angenäherte auflösung von systemen linearer gleichungen. Bull Int Acad Pol Sic Lett A 35:355–357

  • Li W, Wang Q, Bao W, Liu L (2021) On Kaczmarz method with oblique projection for solving large overdetermined linear systems. arXiv preprint arXiv:2106.13368

  • Lin J, Zhou D-X (2015) Learning theory of randomized Kaczmarz algorithm. J Mach Learn Res 16(1):3341–3365

    MathSciNet  Google Scholar 

  • Liu J, Wright S (2016) An accelerated randomized Kaczmarz algorithm. Math Comput 85(297):153–178

    Article  MathSciNet  Google Scholar 

  • Moorman JD, Tu TK, Molitor D, Needell D (2021) Randomized Kaczmarz with averaging. BIT Numer Math 61(1):337–359

    Article  MathSciNet  Google Scholar 

  • Needell D, Ward R (2013) Two-subspace projection method for coherent overdetermined systems. J Fourier Anal Appl 19(2):256–269

    Article  MathSciNet  Google Scholar 

  • Needell D, Zhao R, Zouzias A (2015) Randomized block Kaczmarz method with projection for solving least squares. Linear Algebra Appl 484:322–343

    Article  MathSciNet  Google Scholar 

  • Shao C (2023) A deterministic Kaczmarz algorithm for solving linear systems. SIAM J Matrix Anal Appl 44(1):212–239

    Article  MathSciNet  Google Scholar 

  • Steinerberger S (2021a) Surrounding the solution of a linear system of equations from all sides. Q Appl Math 79:419–429

    Article  MathSciNet  Google Scholar 

  • Steinerberger S (2021b) A weighted randomized Kaczmarz method for solving linear systems. Math Comput 90(332):2815–2826

    Article  MathSciNet  Google Scholar 

  • Strohmer T, Vershynin R (2009) A randomized Kaczmarz algorithm with exponential convergence. J Fourier Anal Appl 15(2):262–278

    Article  MathSciNet  Google Scholar 

  • Tan YS, Vershynin R (2019) Phase retrieval via randomized Kaczmarz: theoretical guarantees. Inf Inference J IMA 8(1):97–123

    MathSciNet  Google Scholar 

  • Wang F, Li W, Bao W, Liu L (2021) Greedy randomized and maximal weighted residual Kaczmarz methods with oblique projection. arXiv preprint arXiv:2106.13606

  • Wen-Ting W (2022) On two-subspace randomized extended Kaczmarz method for solving large linear least-squares problems. Numer Algorithms 89(1):1–31

    Article  MathSciNet  Google Scholar 

  • Yin J-F, Li N, Zheng N (2022) Restarted randomized surrounding methods for solving large linear equations. Appl Math Lett 133:108290

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work is sponsored and supported by the National Natural Science Foundation of China (Grant No. 11971354).

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Correspondence to Jun-Feng Yin.

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Li, N., Yin, JF. The randomized circumcentered-reflection iteration method for solving consistent linear equations. Comp. Appl. Math. 44, 26 (2025). https://doi.org/10.1007/s40314-024-02966-2

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  • DOI: https://doi.org/10.1007/s40314-024-02966-2

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