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  "body": "\n## Summary\n\nThe Krylov subspace of order $r$ generated by a square matrix $A$ and a vector $b$ is the [[linear span|0DTY8B9HMS]] of $b, A b, \\dots, A^{r-1} b$.\nIts elements are the vectors $p(A) b$ for polynomials $p$ of degree less than $r$.\nMany iterative methods for large linear systems and eigenvalue problems search for approximations in Krylov subspaces.\nThese subspaces can be built with matrix-vector products alone.\n\n## Explanations\n\n### L1 human\n\nStart with a vector $b$ and keep applying the matrix: $b$, $A b$, $A^2 b$, and so on.\nThe Krylov subspace is everything one can build from the first few of these vectors by adding and scaling.\nA method that can only multiply by $A$ sees exactly these vectors, so it is natural to search for the answer there.\nFor example, if $A$ is diagonal with entries $1$ and $2$ and $b = (1, 1)$, then $A b = (1, 2)$, and the subspace of order $2$ is all of $\\R^2$.\n",
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