Linear subspace generated from a vector acted on by a power series of a matrix
In linear algebra, the order-rKrylov subspace generated by an n-by-nmatrixA and a vector b of dimension n is the linear subspacespanned by the images of b under the first r powers of A (starting from ), that is,[1][2]
Krylov subspaces are used in algorithms for finding approximate solutions to high-dimensional linear algebra problems.[2] Many linear dynamical system tests in control theory, especially those related to controllability and observability, involve checking the rank of the Krylov subspace. These tests are equivalent to finding the span of the Gramians associated with the system/output maps so the uncontrollable and unobservable subspaces are simply the orthogonal complement to the Krylov subspace.[4]
Modern iterative methods such as Arnoldi iteration can be used for finding one (or a few) eigenvalues of large sparse matrices or solving large systems of linear equations. They try to avoid matrix-matrix operations, but rather multiply vectors by the matrix and work with the resulting vectors. Starting with a vector , one computes , then one multiplies that vector by to find and so on. All algorithms that work this way are referred to as Krylov subspace methods; they are among the most successful methods currently available in numerical linear algebra. These methods can be used in situations where there is an algorithm to compute the matrix-vector multiplication without there being an explicit representation of , giving rise to Matrix-free methods.
^Nocedal, Jorge; Wright, Stephen J. (2006). Numerical optimization. Springer series in operation research and financial engineering (2nd ed.). New York, NY: Springer. p. 108. ISBN978-0-387-30303-1.
^ abSimoncini, Valeria (2015), "Krylov Subspaces", in Nicholas J. Higham; et al. (eds.), The Princeton Companion to Applied Mathematics, Princeton University Press, pp. 113–114
Nevanlinna, Olavi (1993). Convergence of iterations for linear equations. Lectures in Mathematics ETH Zürich. Basel: Birkhäuser Verlag. pp. viii+177 pp. ISBN3-7643-2865-7. MR1217705.
Gerard Meurant and Jurjen Duintjer Tebbens: ”Krylov methods for nonsymmetric linear systems - From theory to computations”, Springer Series in Computational Mathematics, vol.57, (Oct. 2020). ISBN978-3-030-55250-3, url=https://doi.org/10.1007/978-3-030-55251-0.
Iman Farahbakhsh: "Krylov Subspace Methods with Application in Incompressible Fluid Flow Solvers", Wiley, ISBN978-1119618683 (Sep., 2020).