Source [1] is the paper that caused me to create this video. [3], [7] and [8] provided a broad and technical view of randomization as a strategy for NLA. [9] and [12] informed me about the history of NLA. [2], [4], [5], [6], [10], [11], [13] and [14] provide concrete algorithms demonstrating the utility of randomization.

[1] Murray et al. Randomized Numerical Linear Algebra. arXiv:2302.11474v2 2023

[2] Melnichenko et al. CholeskyQR with Randomization and Pivoting for Tall Matrices (CQRRPT). arXiv:2311.08316v1 2023

[3] P. Drineas and M. Mahoney. RandNLA: Randomized Numerical Linear Algebra. Communications of the ACM. 2016

[4] N. Halko, P. Martinsson, and J. Tropp. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions. arXiv:0909.4061v2 2010

[5] Tropp et al. Fixed Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data. NeurIPS Proceedings. 2017

[6] X. Meng, M. Saunders, and M. Mahoney. LSRN: A Parallel Iterative Solver for Strongly Over- Or Underdetermined Systems. SIAM 2014

[7] D. Woodruff. Sketching as a Tool for Numerical Linear Algebra. IBM Research Almaden. 2015

[8] M. Mahoney. Randomized Algorithms for Matrices and Data. arXiv:1104.5557v3. 2011

[9] G. Golub and H van der Vorst. Eigenvalue Computation in the 20th Century. Journal of Computational and Applied Mathematics. 2000

[10] J. Duersch and M. Gu. Randomized QR with Column Pivoting. arXiv:1509.06820v2 2017

[11] Erichson et al. Randomized Matrix Decompositions Using R. Journal of Statistical Software. 2019

[12] J. Gentle et al. Software for Numerical Linear Algebra. Springer. 2017

[13] H. Avron, P. Maymounkov, and S. Toledo. Blendenpik: Supercharging LAPACK's Least-Squares Solver. Siam. 2010

[14] M. Mahoney and P. Drineas. CUR Matrix Decompositions for Improved Data Analysis. Proceedings of the National Academy of Sciences. 2009

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