Giới hạn trên tối tiểu của sai số cắt của thuật toán xấp xỉ ma trận hạng thấp sử dụng phân rã QR có chốt

Björck, Å.: Numerical Methods for Least Squares Problems. SIAM, Philadelphia (1996)

Businger, P., Golub, G.H.: Linear least squares solutions by householder transformations. Numer. Math. 7, 269–276 (1965)

Chandrasekaran, S., Ipsen, I.C.F.: On rank-revealing factorisations. SIAM J. Matrix Anal. Appl. 15(2), 592–622 (1994)

Drmač, Z., Gugercin, S.: A new selection operator for the discrete empirical interpolation method-improved a priori error bound and extensions. SIAM J. Sci. Comput. 38(2), A631–A648 (2016)

Eldén, L.: Numerical linear algebra in data mining. Acta Numer. 15, 327–384 (2006)

Faddeev, D.K., Kublanovskaya, V.N., Faddeeva, V.N.: Solution of linear algebraic systems with rectangular matrices. Trudy Mat. Inst. Steklov. 96, 76–92 (1968)

Golub, G.H.: Numerical methods for solving linear least squares problems. Numer. Math. 7, 206–216 (1965)

Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. The Johns Hopkins University Press, Baltimore (1996)

Hong, Y.P., Pan, C.-T.: Rank-revealing QR factorizations and the singular value decomposition. Math. Comput. 58, 213–232 (1992)

Kahan, W.: Numerical linear algebra. Can. Math. Bull. 9, 757–801 (1966)

Kawamura, H.: Analysis of truncation error of matrix low rank approximation algorithm using QR decomposition with pivot selection. Trans. Jpn. Soc. Ind. Appl. Math. 30(2), 163–176 (2020)

Khoromskij, B.N.: Tensors-structured numerical methods in scientific computing: survey on recent advances. Chemom. Intell. Lab. Syst. 110, 1–19 (2012)

Kumar, N.K., Schneider, J.: Literature survey on low rank approximation of matrices. Linear Multilinear Algebra 65(11), 2212–2244 (2017)

Ye, J.: Generalized low rank approximations of matrices. Mach. Learn. 61, 167–191 (2005)