Structured Low-Rank Matrices and Their Applications
Session Date & Time :
02537 (1/2) : 5C (Aug.25, 13:20-15:00)
02537 (2/2) : 5D (Aug.25, 15:30-17:10)
Type : Proposal of Minisymposium
Abstract : Large dense matrices are ubiquitous in engineering and data science applications, e.g. preconditioners for iterative boundary integral solvers, frontal matrices in sparse multifrontal solvers, and computing the determinant of covariance matrices. Such dense matrices have a numerical low-rank structure, which can be exploited to reduce the complexity of matrix multiplication and factorization from cubic to (near-)linear. As mixed-precision and randomized linear algebra become commonplace, such approximations become increasingly important.