[1]
N. Halko, P. G. Martinsson and J. A. Tropp. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions. SIAM Review 53, 217–288 (2011).
[2]
C. Eckart and G. Young. The Approximation of One Matrix by Another of Lower Rank. Psychometrika 1, 211–218 (1936).
[3]
M. Udell and A. Townsend. Why Are Big Data Matrices Approximately Low Rank? SIAM Journal on Mathematics of Data Science 1, 144–160 (2019).
[4]
[5]
[6]
V. Patel, M. Jahangoshahi and D. A. Maldonado. Randomized Block Adaptive Linear System Solvers. SIAM Journal on Matrix Analysis and Applications 44, 1349–1369 (2023).
[7]
D. P. Woodruff. Sketching as a Tool for Numerical Linear Algebra. Foundations and Trends in Theoretical Computer Science 10, 1–157 (2014).
[8]
N. Ailon and B. Chazelle. The Fast Johnson–Lindenstrauss Transform and Approximate Nearest Neighbors. SIAM Journal on Computing 39, 302–322 (2009).
[9]
J. A. Tropp. Improved Analysis of the Subsampled Randomized Hadamard Transform. Advances in Adaptive Data Analysis 03, 115–126 (2011).
[10]
T. Strohmer and R. Vershynin. A Randomized Kaczmarz Algorithm with Exponential Convergence. Journal of Fourier Analysis and Applications 15, 262–278 (2009).
[11]
T. S. Motzkin and I. J. Schoenberg. The Relaxation Method for Linear Inequalities. Canadian Journal of Mathematics 6, 393–404 (1954).
[12]
[13]
D. Needell and J. A. Tropp. Paved with Good Intentions: Analysis of a Randomized Block Kaczmarz Method. Linear Algebra and its Applications 441, 199–221 (2014).