Alphabeta Math
TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passaudited 2026-08-31
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Near a simple Hermitian eigenvector, Rayleigh-quotient iteration converges cubically

Statement

Let F∈{R,C}, let A∈Mn(F) be self-adjoint (Hermitian in the complex case), and let q∈Fn be a unit eigenvector for a simple eigenvalue λ. Then there are constants C>0 and δ>0 such that whenever

0<dist⁡(xk,Fq)<δ

and the Rayleigh-quotient step of Rayleigh-quotient iteration for Hermitian matrices is defined, the next iterate satisfies

dist⁡(xk+1,Fq)≤C dist⁡(xk,Fq)3.

Thus the convergence to the eigendirection is local and cubic.

Facts & Assumptions

Given: A Hermitian matrix A, a simple eigenpair (λ,q), and a Rayleigh-quotient iterate xk with 0<dist⁡(xk,Fq)<δ for δ small enough that the current Rayleigh-quotient step is defined.

[L3]

Rayleigh-quotient iteration uses the current Rayleigh quotient as the shift (Rayleigh-quotient iteration for Hermitian matrices).

[L4]

Orthogonal projection onto a one-dimensional subspace gives the nearest point on that subspace; for a unit vector q it is PFqx=⟨x,q⟩q (Orthogonal projection is linear, and an orthonormal basis (ei) of W gives PWv=∑i⟨v,ei⟩ei, The orthogonal projection is the unique nearest point in the subspace).

Proof

technique · direct
1.1L1L2L4given

By [L1] and [L2], choose an orthonormal eigenbasis q,q2,…,qn over the given field, with eigenvalues λ,λ2,…,λn. Let γ:=min⁡i≥2∣λi−λ∣>0. For θk:=dist⁡(xk,Fq), [L4] gives the orthogonal decomposition xk=ckq+zk,zk⊥q,∥zk∥2=θk. Since the iterate has unit length, ∣ck∣2+θk2=1.

2.1L1L5step 1.1algebra

The Rayleigh shift is μk:=⟨Axk,xk⟩. Because Aq=λq, zk⊥q, and A is Hermitian, the cross terms vanish: μk=∣ck∣2λ+⟨Azk,zk⟩. Using ∣ck∣2+θk2=1, this becomes μk−λ=⟨Azk,zk⟩−λθk2. Hence [L5] gives ∣μk−λ∣≤(∥A∥2+∣λ∣)θk2. Set C0:=∥A∥2+∣λ∣, and shrink δ so that C0δ2≤γ/2 and δ≤1/2. Then whenever θk<δ, the shift satisfies ∣μk−λ∣≤γ/2, so ∣λi−μk∣≥γ/2 for every i≥2.

3.1L2L3step 1.1step 2.1algebra

By [L3], the next unnormalised iterate is yk+1:=(A−μkI)−1xk. Using the eigenbasis from step 1.1, yk+1=ck(λ−μk)−1q+wk,wk:=(A−μkI)−1zk∈q⊥. Because ∣λi−μk∣≥γ/2 on q⊥, one has ∥wk∥2≤2γ θk.

4.1step 2.1step 3.1algebra

Since θk<δ≤1/2, the identity ∣ck∣2+θk2=1 gives ∣ck∣≥3/2. After normalising yk+1, the distance to the line Fq is bounded by the ratio of the orthogonal and parallel parts: θk+1≤∥wk∥2∣ck∣ ∣λ−μk∣−1≤43 γ ∣λ−μk∣ θk. Combining this with step 2.1 yields θk+1≤4C03 γ θk3.

5.1step 4.1∎

Taking C:=4C0/(3 γ) proves dist⁡(xk+1,Fq)≤C dist⁡(xk,Fq)3 whenever 0<dist⁡(xk,Fq)<δ and the current Rayleigh-quotient step is defined. Hence the convergence is local and cubic.

Depends on

Used by

Dependency tree · two levels

23 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources