Polynomial time $$\rho $$-locally maximum volume search
Author:
Funder
Russian Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Algebra and Number Theory
Link
https://link.springer.com/content/pdf/10.1007/s10092-023-00536-2.pdf
Reference21 articles.
1. Bebendorf, M., Rjasanow, S.: Adaptive low-rank approximation of collocation matrices. Computing 70, 1–24 (2003). https://doi.org/10.1007/s00607-002-1469-6
2. Goreinov, S.A., Oseledets, I.V., Savostyanov, D.V., Tyrtyshnikov, E.E., Zamarashkin, N.L.: How to find a good submatrix. In: Matrix Methods: Theory, Algorithms, Applications, pp. 247–256 (2010). https://doi.org/10.1142/9789812836021_0015
3. Oseledets, I.V., Tyrtyshnikov, E.E.: TT-cross approximation for multidimensional arrays. Linear Algebra Appl. 432, 70–88 (2010). https://doi.org/10.1016/J.LAA.2009.07.024
4. Goreinov, S.A., Tyrtyshnikov, E.E.: The maximal-volume concept in approximation by low-rank matrices. Contemp. Math. 268, 47–51 (2001). https://doi.org/10.1090/conm/280
5. Goreinov, S.A., Tyrtyshnikov, E.E.: Quasioptimality of skeleton approximation of a matrix in the Chebyshev norm. Dokl. Math. 83(3), 1–2 (2011). https://doi.org/10.1134/S1064562411030355
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