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.
Finite Hermitian ground vectors and a deterministic tie rule
Statement
Let , let , and let satisfy . Define Then has a minimum , equal to the least eigenvalue of , and its minimizers are precisely the nonzero vectors in the lowest eigenspace . There is a deterministic choice of a unit minimizer: let be the orthogonal projection onto , scan the standard basis , and take the first with . Normalize ; over , if is its first nonzero coordinate, multiply by , and over multiply by .
Facts & Assumptions
Given: A real symmetric or complex Hermitian matrix with .
Self-adjoint and normal endomorphisms of a finite-dimensional real or complex inner product space: self-adjoint means , and every self-adjoint endomorphism is normal.
In an orthonormal basis, self-adjoint means conjugate-transpose symmetry and normal means commuting with the conjugate transpose: in an orthonormal basis, self-adjointness is equivalent to the matrix identity .
The Rayleigh quotient of a nonzero vector for a self-adjoint endomorphism: on a finite-dimensional real inner-product space, for .
Courant-Fischer min-max principle for self-adjoint endomorphisms on finite-dimensional real inner product spaces: for a self-adjoint endomorphism of an -dimensional real inner-product space, the least eigenvalue is the minimum of its Rayleigh quotient over nonzero vectors.
Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis: a finite-dimensional real self-adjoint endomorphism has an orthonormal eigenbasis.
Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely: a normal endomorphism of a finite-dimensional complex inner-product space has an orthonormal eigenbasis.
For a subspace of a finite-dimensional inner product space, : a subspace of a finite-dimensional inner-product space has an orthogonal complement, and every vector has a unique decomposition into the subspace and its orthogonal complement.
Proof
Suppose . By [F2], represents a self-adjoint endomorphism in the standard orthonormal basis, and [F3] identifies its Rayleigh quotient with . Taking in [F4] gives . On each one-dimensional , homogeneity gives the same quotient for every nonzero vector, and every nonzero spans such a ; hence this minimum is exactly . By [F5], write in an orthonormal eigenbasis with real eigenvalues ; then , which equals exactly when every nonzero coefficient belongs to the least-eigenvalue eigenspace.
Suppose . By [F2], represents a self-adjoint endomorphism in the standard orthonormal basis; by [F1] this endomorphism is normal, so [F6] gives an orthonormal eigenbasis . Every eigenvalue is real because if , then , so is real and . For , the quotient is the weighted average , so it is at least the least eigenvalue and equals it exactly when all nonzero coefficients lie in its eigenspace.
In either field, the least eigenspace is nonzero, so [F7] gives a unique orthogonal decomposition of each vector into its and parts; define to be the first component. Uniqueness makes linear, since sums and scalar multiples of decompositions are the corresponding decompositions. At least one is nonzero, since otherwise linearity and the fact that the standard vectors span would imply , contradicting . The first such is uniquely determined by the fixed basis order. Normalize this projection. Over , its first nonzero coordinate is changed to by multiplication by ; over , multiplication by makes that coordinate positive. These unit-modulus rescalings preserve the eigenvector and its Rayleigh quotient. The minimizer characterization in steps 1.1 and 1.2 proves the result.
Depends on
- Self-adjoint and normal endomorphisms of a finite-dimensional real or complex inner product space
- In an orthonormal basis, self-adjoint means conjugate-transpose symmetry and normal means commuting with the conjugate transpose
- The Rayleigh quotient of a nonzero vector for a self-adjoint endomorphism
- 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
- Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely
- For a subspace $W$ of a finite-dimensional inner product space, $V=W\oplus W^\perp$
Used by
- Finite alternating tensor optimization with effective eigenproblems Algorithm
- A degenerate local ground eigenspace has a deterministic tie choice Example
- A two-site TT truncation can increase energy after an exact solve Example
- An exact local DMRG ground solve does not increase Rayleigh energy Proposition
- Convergent fixed-rank exact sweeps are coordinatewise stationary Theorem
Dependency tree · two levels
19 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
- Ulrich Schollwöck, The density-matrix renormalization group in the age of matrix product states, Annals of Physics 326 (2011), §§6.3–6.4 (standard reference, not scraped)
- Grégoire Misguich, Introduction to matrix-product states and tensor networks, complete lecture notes (2026), §2.7 (standard reference, not scraped)