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 …

We develop and analyze a method to reduce the size of a very large set of data points in a
high-dimensional Euclidean space R^d to a small set of weighted points such that the result …

Big data can be a blessing: with very large training data sets it becomes possible to perform
complex learning tasks with unprecedented accuracy. Yet, this improved performance …

Given a set F of n positive functions over a ground set X, we consider the problem of
computing x* that minimizes the expression∑ f∈ Ff (x), over x∈ X. A typical application is …

Given a set of $ n $ points in $ d $ dimensions, the Euclidean $ k $-means problem (resp.
Euclidean $ k $-median) consists of finding $ k $ centers such that the sum of squared …

The (k, z)-clustering problem consists of finding a set of k points called centers, such that the
sum of distances raised to the power of z of every data point to its closest center is …

We investigate coresets-succinct, small summaries of large data sets-so that solutions found
on the summary are provably competitive with solution found on the full data set. We provide …

We consider low-rank reconstruction of a matrix using a subset of its columns and present
asymptotically optimal algorithms for both spectral norm and Frobenius norm reconstruction …

A coreset for a set of points is a small subset of weighted points that approximately
preserves important properties of the original set. Specifically, if $ P $ is a set of points, $ Q …

In optimization or machine learning problems we are given a set of items, usually points in
some metric space, and the goal is to minimize or maximize an objective function over some …