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Lanczos largest Ritz values obey a Chebyshev start-angle bound, including degenerate branches
Statement
Let , , be symmetric with ordered eigenvalues . Let be the multiplicity of . If , assume , and let be the orthogonal projection onto the -eigenspace. Let be a unit vector and, for , define
Thus is the largest Rayleigh–Ritz value on ; if Lanczos runs through step without breakdown, it is the largest Ritz value of . If , then and for every . Suppose .
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If , then for every ; no convergence to is asserted.
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If , put If , then for every , For a simple top eigenvalue (), is the tangent of the angle between and a unit top eigenvector, so the starting-angle denominator is explicit.
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If and , then
When , , and exact Lanczos breakdown occurs at dimension , then and for all . These are exact-arithmetic claims; they give no finite-precision grade-termination guarantee.
Facts & Assumptions
Given: A real symmetric , its ordered eigenvalues, the top-eigenspace projection when , a unit start vector , the Krylov spaces , and the largest Rayleigh–Ritz values defined in the Statement.
The Lanczos process as Arnoldi specialized to a Hermitian matrix, with the usual real-subdiagonal phase convention: exact Lanczos is Arnoldi applied to a symmetric matrix, with real nonnegative subdiagonal coefficients before breakdown.
Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization: before breakdown, the Arnoldi vectors form an orthonormal basis of and satisfy .
Ritz values and Ritz vectors extracted from the Arnoldi Hessenberg reduction and An Arnoldi Ritz pair has residual norm controlled by the last Hessenberg subdiagonal entry: eigenvalues of are Ritz values with vectors ; if Arnoldi runs through step without breakdown and , then .
Courant-Fischer min-max principle for self-adjoint endomorphisms on finite-dimensional real inner product spaces and Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis: every real symmetric compression has a largest Rayleigh–Ritz value equal to its largest eigenvalue, and has an orthonormal eigenbasis.
A scaled Chebyshev polynomial suppresses a separated interval: for , the degree- scaled filter has value at and absolute value at most on .
The Arnoldi process for building an orthonormal basis of a Krylov subspace: each exact Arnoldi column has ; exact breakdown at column means .
Proof
If exact Lanczos runs through step without breakdown, [F1], [F2], and [F7] give an orthonormal basis of and a symmetric compression . By [F3], each eigenpair of lifts to an exact Ritz pair of ; by [F4], the largest eigenvalue of is the maximum Rayleigh quotient on , namely . If Lanczos breaks down earlier, no step- basis or Ritz assertion is used; remains defined directly by the Rayleigh–Ritz formula in the Statement.
Assume . By [F4], choose an orthonormal eigenbasis of whose first vectors span the range of . If , then is orthogonal to that eigenspace. For every integer and every top eigenvector , symmetry gives . Thus every vector in lies in the orthogonal complement of the top eigenspace, where all Rayleigh quotients are at most ; this proves branch 1.
If , every eigenvalue is , so [F4] gives . Every nonzero then has Rayleigh quotient , proving the stated dimension-one/full-multiplicity branch.
Assume and . Apply [F5] with , , , and to obtain a polynomial of degree at most with and on , where . By [F7], belongs to , and spectral decomposition gives and .
Assume and . The spectral decomposition gives . Hence , so [F7] gives and it has Rayleigh quotient ; this proves for . For , [F7] gives , and the same decomposition gives the displayed exact formula for .
Finally, assume , , and exact Lanczos breaks down at column . For , no breakdown occurs at columns . Starting from , inductively suppose form an orthonormal basis of . By the exact Arnoldi definition [F1, F6], the residual lies in , is orthogonal to , and is nonzero for ; hence its normalized vector lies in . Since is generated by vectors, form a basis of . Thus form a basis of . For , directly spans . Each for , and because its orthogonalized residual is zero. Hence , and [F7] plus induction gives for .
In the eigenbasis of [F4], write . The two components are orthogonal, , and all eigenvalues on the second component lie in . Therefore Since is the maximum Rayleigh quotient on , it is at least the quotient of this nonzero and at most . This proves branch 2, including because .
Let be the distinct eigenvalues of . Since , and the denominators below are nonzero. Define Then and for . On the orthonormal eigenbasis from [F4], therefore acts as the identity on the -eigenspace and as zero on every other eigenspace, so . Since and , every power of applied to lies in , whence . The nonzero vector has Rayleigh quotient , while no quotient can exceed ; thus and the exact-breakdown branch follows.
Depends on
- The Krylov subspace $K_m(A,b)=\operatorname{span}\{b,Ab,\dots,A^{m-1}b\}$
- The Arnoldi process for building an orthonormal basis of a Krylov subspace
- The Lanczos process as Arnoldi specialized to a Hermitian matrix, with the usual real-subdiagonal phase convention
- Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization
- Ritz values and Ritz vectors extracted from the Arnoldi Hessenberg reduction
- An Arnoldi Ritz pair has residual norm controlled by the last Hessenberg subdiagonal entry
- Courant-Fischer min-max principle for self-adjoint endomorphisms on finite-dimensional real inner product spaces
- Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis
- A scaled Chebyshev polynomial suppresses a separated interval
Used by
- A Lanczos Ritz value can miss the operator norm Example
- A poorly aligned start delays the largest Ritz value and requires the start-angle factor Example
- A trace eigenspace objective and a Lanczos start-angle bound Example
- Lanczos handles repeated top eigenvalues, absent top components, and exact breakdown Example
Dependency tree · two levels
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Sources
- Yousef Saad, Iterative Methods for Sparse Linear Systems, 2nd ed., §6.6 and §6.11.1, printed pp. 194–195 and 209–210 (standard reference, not scraped)
- David Bindel, Matrix Computations (CS 6210), Cornell University, Fall 2012, Lectures 38–39 (standard reference, not scraped)