Low-rank matrix approximations, such as the truncated singular value decomposition and
the rank-revealing QR decomposition, play a central role in data analysis and scientific …

Low rank approximation of matrices has been well studied in literature. Singular value
decomposition, QR decomposition with column pivoting, rank revealing QR factorization …

Drug resistance in cancer is often linked to changes in tumor cell state or lineage, but the
molecular mechanisms driving this plasticity remain unclear. Using murine organoid and …

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 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 …

Randomized numerical linear algebra-RandNLA, for short-concerns the use of
randomization as a resource to develop improved algorithms for large-scale linear algebra …

We improve a recent gurantee of Bach and Moulines on the linear convergence of SGD for
smooth and strongly convex objectives, reducing a quadratic dependence on the strong …

Randomized algorithms for very large matrix problems have received a great deal of
attention in recent years. Much of this work was motivated by problems in large-scale data …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

The statistical leverage scores of a matrix A are the squared row-norms of the matrix
containing its (top) left singular vectors and the coherence is the largest leverage score …