Hossein Esfandiari, Mohammad Taghi, et al.
SPAA 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.
Hossein Esfandiari, Mohammad Taghi, et al.
SPAA 2016
Kenneth L. Clarkson, David P. Woodruff
SODA 2015
Piotr Indyk, Eric Price, et al.
FOCS 2011
Kenneth L. Clarkson, David P. Woodruff
STOC 2009