A general error analysis for randomized low-rank approximation with application to data assimilation

arXiv:2405.04811v1 Announce Type: cross
Abstract: Randomized algorithms have proven to perform well on a large class of numerical linear algebra problems. Their theoretical analysis is critical to provide guarantees on their behaviour, and in this sense, the stochastic analysis of the randomized low-rank approximation error plays a central role. Indeed, several randomized methods for the approximation of dominant eigen- or singular modes can be rewritten as low-rank approximation methods. However, despite the large variety of algorithms, the existing theoretical frameworks …

abstract algebra algorithms analysis application approximation arxiv class cs.na data error general linear linear algebra low math.na numerical sense stat.ml stochastic type