@article{42b9f08c20464e899f19739840dd31ba,

title = "Low-rank updates of matrix square roots",

abstract = "Models in which the covariance matrix has the structure of a sparse matrix plus a low rank perturbation are ubiquitous in data science applications. It is often desirable for algorithms to take advantage of such structures, avoiding costly matrix computations that often require cubic time and quadratic storage. This is often accomplished by performing operations that maintain such structures, for example, matrix inversion via the Sherman–Morrison–Woodbury formula. In this article, we consider the matrix square root and inverse square root operations. Given a low rank perturbation to a matrix, we argue that a low-rank approximate correction to the (inverse) square root exists. We do so by establishing a geometric decay bound on the true correction's eigenvalues. We then proceed to frame the correction as the solution of an algebraic Riccati equation, and discuss how a low-rank solution to that equation can be computed. We analyze the approximation error incurred when approximately solving the algebraic Riccati equation, providing spectral and Frobenius norm forward and backward error bounds. Finally, we describe several applications of our algorithms, and demonstrate their utility in numerical experiments.",

keywords = "low rank perturbations, low rank updates, matrix functions, matrix square root",

author = "Shany Shmueli and Petros Drineas and Haim Avron",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors. Numerical Linear Algebra with Applications published by John Wiley & Sons Ltd.",

year = "2024",

month = jan,

doi = "10.1002/nla.2528",

language = "אנגלית",

volume = "31",

journal = "Numerical Linear Algebra with Applications",

issn = "1070-5325",

publisher = "John Wiley and Sons Ltd",

number = "1",

}