Randomized block Krylov subspace methods form a powerful class of algorithms for computing the extreme eigenvalues symmetric matrix or singular values general matrix. The purpose this paper is to develop new theoretical bounds on performance randomized these problems. For matrices with polynomial spectral decay, method can obtain an accurate norm estimate using only constant number steps (that ...

In the literature, there exist several studies on symbol-based multigrid methods for solution of linear systems having structured coefficient matrices. particular, convergence analysis such has been obtained in an elegant form case Toeplitz matrices generated by a scalar-valued function. block-Toeplitz setting, that is, where matrix entries are small generic instead scalars, some algorithms hav...