Krylov subspace

The concept is named after Russian applied mathematician and naval engineer Alexei Krylov, who published a paper about it in 1931.[2]

• ${\displaystyle {\mathcal {K}}_{r}(A,b),A{\mathcal {K}}_{r}(A,b)\subset {\mathcal {K}}_{r+1}(A,b)}$.
• Vectors ${\displaystyle \{b,Ab,A^{2}b,\ldots ,A^{r-1}b\}}$ are linearly independent until ${\displaystyle r<r_{0}}$, and ${\displaystyle {\mathcal {K}}_{r}(A,b)\subset {\mathcal {K}}_{r_{0}}(A,b)}$. ${\displaystyle r_{0}}$ is the maximal dimension of a Krylov subspace.
• For such ${\displaystyle r_{0}}$ we have ${\displaystyle r_{0}\leq 1+\mathrm {rank} A}$ and ${\displaystyle r_{0}\leq n}$, more exactly ${\displaystyle r_{0}\leq \partial p(A)}$, where ${\displaystyle p(A)}$ is the minimal polynomial of ${\displaystyle A}$.
• There exists a ${\displaystyle b}$ such that ${\displaystyle r_{0}=\partial p(A)}$.
• ${\displaystyle {\mathcal {K}}_{r}(A,b)}$ is a cyclic submodule generated by ${\displaystyle b}$ of the torsion ${\displaystyle k[x]}$-module ${\displaystyle (k^{n})^{A}}$, where ${\displaystyle k^{n}}$ is the linear space on ${\displaystyle k}$.
• ${\displaystyle k^{n}}$ can be decomposed as the direct sum of Krylov subspaces.

Krylov subspaces are used in algorithms for finding approximate solutions to high-dimensional linear algebra problems.[1]

Modern iterative methods for finding one (or a few) eigenvalues of large sparse matrices or solving large systems of linear equations avoid matrix-matrix operations, but rather multiply vectors by the matrix and work with the resulting vectors. Starting with a vector, b, one computes ${\displaystyle Ab}$, then one multiplies that vector by ${\displaystyle A}$ to find ${\displaystyle A^{2}b}$ 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.

Because the vectors usually soon become almost linearly dependent due to the properties of power iteration, methods relying on Krylov subspace frequently involve some orthogonalization scheme, such as Lanczos iteration for Hermitian matrices or Arnoldi iteration for more general matrices.

The best known Krylov subspace methods are the Arnoldi, Lanczos, Conjugate gradient, IDR(s) (Induced dimension reduction), GMRES (generalized minimum residual), BiCGSTAB (biconjugate gradient stabilized), QMR (quasi minimal residual), TFQMR (transpose-free QMR), and MINRES (minimal residual) methods.

• Iterative method, which has a section on Krylov subspace methods

• Nevanlinna, Olavi (1993). Convergence of iterations for linear equations. Lectures in Mathematics ETH Zürich. Basel: Birkhäuser Verlag. pp. viii+177 pp. ISBN 3-7643-2865-7. MR 1217705.
• Saad, Yousef (2003). Iterative methods for sparse linear systems (2nd ed.). SIAM. ISBN 0-89871-534-2. OCLC 51266114.
• 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). ISBN 978-3-030-55250-3, url=https://doi.org/10.1007/978-3-030-55251-0.
• Iman Farahbakhsh: "Krylov Subspace Methods with Application in Incompressible Fluid Flow Solvers", Wiley, ISBN 978-1119618683 (Sep., 2020).