Alphabeta Math
AlgorithmAlgorithm: AI-adaptedProof: Not applicablePipeline-generatedverified 2026-10-22 (gpt-6.1-sol)
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.

Conforming Rayleigh–Ritz procedure

Definition

Let H be a nonzero complex Hilbert space whose inner product is linear in its first argument. Let Q⊆H be a dense linear subspace, and let q:Q×Q→C be a Hermitian sesquilinear form: it is linear in its first argument, conjugate-linear in its second, and q(u,v)=q(v,u)‾ for all u,v∈Q. In particular, q(u,u)∈R. Write q[u]=q(u,u). Assume q[u]≥−c∥u∥2 for some real c≥0, and assume Q is complete in the form norm ∥u∥q,c2=q[u]+(c+1)∥u∥2; this is the meaning of a closed semibounded form here.

For an integer n≥1, let ϕ1,…,ϕn∈Q be linearly independent. The exact input matrices are the Gram matrix G and form matrix A with entries

Gij=⟨ϕj,ϕi⟩,Aij=q(ϕj,ϕi)(1≤i,j≤n).

For every nonzero coefficient vector z∈Cn, z∗Gz=∥∑jzjϕj∥2>0, so G is positive definite; the symmetry of q makes A Hermitian.

The procedure is:

  1. Form G and A from the supplied vectors and form, using exact entries.
  2. Diagonalize the positive definite Hermitian matrix G and form its unique positive inverse square root G−1/2. Set B=G−1/2AG−1/2.
  3. Diagonalize the Hermitian matrix B, listing its real eigenvalues ρ1≤⋯≤ρn with multiplicity and selecting an orthonormal eigenbasis y1,…,yn. For each j, set cj=G−1/2yj and return (ρj,cj).

Repeated eigenvalues may return any orthonormal basis of the corresponding eigenspace. The procedure uses trial vectors in Q and does not require them to lie in the operator domain of an associated self-adjoint operator. It operates on exact matrix entries: if quadrature or an approximate eigensolver supplies numerical data, this procedure alone supplies no error enclosure or certification for those approximations.

Remarks

Conformity means exactly that each trial vector lies in the stated form domain. A nonconforming grid vector is not automatically an element of that continuum form domain. The procedure returns the exact eigenvalues of its supplied matrices; any certified numerical Ritz interval must enclose integration error in both G and A and include the numerical eigensolver error separately.

Depends on

Dependency tree · 2 levels

Sources