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 …

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 …

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 …

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 …

We design a new distribution over m× n matrices S so that, for any fixed n× d matrix A of rank
r, with probability at least 9/10,∥ SAx∥ 2=(1±ε)∥ Ax∥ 2 simultaneously for all x∈ R d …

In this paper, we present a new algorithm for'/solving linear programs that requires only Õ
(√ rank (A) L) iterations where A is the constraint matrix of a linear program with m …

We show how to approximate a data matrix A with a much smaller sketch~ A that can be
used to solve a general class of constrained k-rank approximation problems to within (1+ ε) …