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Arnoldi reduces GMRES to a least-squares problem for the small Hessenberg matrix
Statement
Let and set . Run Arnoldi on through step without breakdown, so that . Then every vector has the form for some , and
Consequently,
Facts & Assumptions
Given: A linear system , an initial guess , the nonzero initial residual , and an Arnoldi run on through step without breakdown.
A GMRES iterate minimizes the residual norm over (The GMRES iterate as the residual minimizer over an affine Krylov space).
Arnoldi gives an orthonormal basis of and the factorization with (Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization).
Proof
By [L1], the columns of form a basis of , so every can be written uniquely as . Since and , one has
The columns of are orthonormal by [L1], so multiplying by preserves the Euclidean norm. Therefore Taking minima over the corresponding parametrized sets gives the stated small least-squares problem. This is the residual norm whose minimizers [F1] calls GMRES iterates.
Depends on
- The GMRES iterate as the residual minimizer over an affine Krylov space
- Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization
- For a linear map $T:V\to W$ between finite-dimensional inner-product spaces, $x$ minimises $\lVert Tx-b\rVert$ if and only if $T^*(Tx-b)=0$, equivalently $T^*Tx=T^*b$; minimisers exist and any two differ by an element of $\ker T$
Used by
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