RandNLA: randomized numerical linear algebra Page 1 80 COMMUNICATIONS OF THE ACM
| JUNE 2016 | VOL. 59 | NO. 6 review articles DOI:10.1145/2842602 Randomization offers new …

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a
powerful alternative to unsupervised analysis. However, most reference datasets are …

Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …

This survey describes probabilistic algorithms for linear algebraic computations, such as
factorizing matrices and solving linear systems. It focuses on techniques that have a proven …

This article shows how to solve linear programs of the form min Ax= b, x≥ 0 c⊤ x with n
variables in time O*((n ω+ n 2.5− α/2+ n 2+ 1/6) log (n/δ)), where ω is the exponent of matrix …

In this paper we provide new randomized algorithms with improved runtimes for solving
linear programs with two-sided constraints. In the special case of the minimum cost flow …

Random matrices now play a role in many areas of theoretical, applied, and computational
mathematics. Therefore, it is desirable to have tools for studying random matrices that are …

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 …

This tutorial text gives a unifying perspective on machine learning by covering both
probabilistic and deterministic approaches-which are based on optimization techniques …

Online matrix vector multiplication is a fundamental step and bottleneck in many machine
learning algorithms. It is defined as follows: given a matrix at the pre-processing phase, at …