How to fake multiply by a Gaussian matrix
Michael Kapralov, Vamsi K. Potluru, et al.
ICML 2016
This survey highlights the recent advances in algorithms for numerical linear algebra that have come from the technique of linear sketching, whereby given a matrix, one first compresses it to a much smaller matrix by multiplying it by a (usually) random matrix with certain properties. Much of the expensive computation can then be performed on the smaller matrix, thereby accelerating the solution for the original problem. In this survey we consider least squares as well as robust regression problems, low rank approximation, and graph sparsification. We also discuss a number of variants of these problems. Finally, we discuss the limitations of sketching methods.
Michael Kapralov, Vamsi K. Potluru, et al.
ICML 2016
Arnab Bhattacharyya, Elena Grigorescu, et al.
SIAM Journal on Computing
Moritz Hardt, David P. Woodruff
STOC 2013
Alexandr Andoni, Jiecao Chen, et al.
ITCS 2016