- [1]
- [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]
- S. Park and S.-M. Moon. CURing Large Models: Compression via CUR Decomposition (Jan 2025), arXiv:2501.04211 [cs]. ↩1
- [5]
- P.-G. Martinsson and J. Tropp. Randomized Numerical Linear Algebra: Foundations & Algorithms (2020). ↩1 ↩2
- [6]
- [7]
- D. P. Woodruff. Sketching as a Tool for Numerical Linear Algebra. Foundations and Trends in Theoretical Computer Science 10, 1–157 (2014). ↩1
- [8]
- N. Ailon and B. Chazelle. The Fast Johnson–Lindenstrauss Transform and Approximate Nearest Neighbors. SIAM Journal on Computing 39, 302–322 (2009). ↩1 ↩2 ↩3
- [9]
- J. A. Tropp. Improved Analysis of the Subsampled Randomized Hadamard Transform. Advances in Adaptive Data Analysis 03, 115–126 (2011). ↩1
- [10]
- T. Strohmer and R. Vershynin. A Randomized Kaczmarz Algorithm with Exponential Convergence. Journal of Fourier Analysis and Applications 15, 262–278 (2009). ↩1 ↩2
- [11]
- S. Agmon. The Relaxation Method for Linear Inequalities. Canadian Journal of Mathematics 6, 382–392 (1954). ↩1
- [12]
- J. Haddock and A. Ma. Greed Works: An Improved Analysis of Sampling Kaczmarz–Motzkin. SIAM Journal on Mathematics of Data Science 3, 342–368 (2021). ↩1
- [13]
- T. S. Motzkin and I. J. Schoenberg. The Relaxation Method for Linear Inequalities. Canadian Journal of Mathematics 6, 393–404 (1954). ↩1 ↩2
- [14]
- M. Pilanci and M. J. Wainwright. Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares (2014). ↩1
- [15]
- 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). ↩1