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 …

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 …

We describe a new algorithm called FrequentDirections for deterministic matrix sketching in
the row-update model. The algorithm is presented an arbitrary input matrix A ∈ R^ n * d one …

We analyze a compression scheme for large data sets that randomly keeps a small
percentage of the components of each data sample. The benefit is that the output is a sparse …

Sinkhorn algorithm has been used pervasively to approximate the solution to optimal
transport (OT) and unbalanced optimal transport (UOT) problems. However, its practical …

Graph Transformers excel in long-range dependency modeling, but generally require
quadratic memory complexity in the number of nodes in an input graph, and hence have …

Let $\mathbf S\in\mathbb R^{n\times n} $ satisfy $\|\mathbf 1-\mathbf S\| _2\le\epsilon n $,
where $\mathbf 1$ is the all ones matrix and $\|\cdot\| _2 $ is the spectral norm. It is well …

Given an order-tensor, we present a simple, element-wise sparsification algorithm that
zeroes out all sufficiently small elements of, keeps all sufficiently large elements of and …

A graph is a flexible data structure that can represent relationships between entities. As with
other data analysis tasks, the use of realistic graphs is critical to obtaining valid research …

We consider the problem of matrix column subset selection, which selects a subset of
columns from an input matrix such that the input can be well approximated by the span of the …