3 accepted papers
In order to compute fast approximations to the singular value decompositions (SVD) of very large matrices, randomized sketching algorithms have become a leading approach. However, a key practical difficulty of sketching an SVD is that the user does not know how far the sketched singular vectors/valu…
Randomized Newton methods have recently become the focus of intense research activity in large-scale and distributed optimization. In general, these methods are based on a “computation-accuracy trade-off”, which allows the user to gain scalability in exchange for error in the solution. However, the…
Over the course of the past decade, a variety of randomized algorithms have been proposed for computing approximate least-squares (LS) solutions in large-scale settings. A longstanding practical issue is that, for any given input, the user rarely knows the actual error of an approximate solution (re…