Alphabeta Math
LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)
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  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
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Positive definiteness is exactly the inverse condition for the observed GP covariance

Statement

In the finite observation model, let K=KXX and let R be symmetric and positive semidefinite. Then A=K+R is symmetric and positive semidefinite. Its ordinary inverse exists if and only if A is positive definite, equivalently if and only if all singular values of A are positive. Thus the standard inverse formulas are defined exactly under A≻0; no ordinary inverse formula applies when A is singular.

Facts & Assumptions

Given: A finite observation matrix K=KXX from a symmetric positive-semidefinite kernel and a symmetric positive-semidefinite noise covariance R.

[F1]

Every finite Gram matrix of the kernel is positive semidefinite, and the observed matrix is A=KXX+R. Finite Gaussian-process regression observation model

[F3]

The singular values of an endomorphism are the eigenvalues of ∣A∣=A∗A. The singular values of a linear map as the eigenvalues of the positive square root of T^*T

[F4]

A nonnegative finite-dimensional operator has a unique nonnegative square root. A non-negative operator has a unique non-negative square root

[F5]

A finite-dimensional linear map has a singular-value decomposition with exactly its positive singular values in the nonzero diagonal block. Every linear map between finite-dimensional real or complex inner product spaces admits a singular value decomposition

Proof

Given: The matrices K and R in the Statement.

1.1F1givenalgebra

For every z∈Rn, zTAz=zTKz+zTRz≥0 by [F1], and A is symmetric. Hence A is a nonnegative endomorphism of the finite-dimensional real inner-product space Rn.

2.1F2step 1.1algebra

By [F2], choose an orthonormal eigenbasis e1,…,en of A with eigenvalues λj≥0. In this basis, A is invertible exactly when no λj is zero, while zTAz=∑jλjcj2 for z=∑jcjej is positive for every nonzero z exactly when all λj>0. Thus A is invertible if and only if it is positive definite.

3.1F3F4F5step 2.1algebra∎

In the same basis, A∗A=A2 has eigenvalues λj2. By [F4], its unique nonnegative square root has eigenvalues λj, so [F3] says the singular values of A are precisely these λj. By [F5], the SVD has full rank exactly when all n singular values are positive, which is exactly the invertibility condition in step 2.1.

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