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TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)
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Correctness of finite Gaussian block elimination

Statement

Assume the Axiom of Countable Choice ACω and the hypotheses of Exact finite Gaussian block elimination with input K0 and retained sets R0,…,Rq. Let Kj,Cj,ℓj be the algorithm outputs. At each stage write Ej=Rj−1∖Rj, and use the empty-set conventions of the algorithm. Then the algorithm terminates after q stages, every nonempty retained precision Kj is positive definite, and for every fixed retained vector x∈RRq,

∫R⋃j=1qEjexp⁡ ⁣(−12z~(x,y)TK0z~(x,y)) dλ(y)=exp⁡ ⁣(∑j=1qℓj)exp⁡ ⁣(−12xTKqx),

where y lists the eliminated coordinates in increasing order and z~(x,y)∈RR0 is the unique full coordinate vector agreeing with x on Rq and with y on R0∖Rq. The measure λ is Euclidean Lebesgue measure when the eliminated-coordinate space has positive dimension; if ⋃jEj=∅, identify R⋃jEj with the singleton R0={()} and let λ assign mass one to it. If Rq≠∅, the normalized marginal law of the retained coordinates under γK0 is exactly γKq; if Rq=∅, the final retained space is the singleton R0 and the identity gives ZK0=exp⁡(∑jℓj). In all cases,

ln⁡ZK0=∑j=1qℓj+ln⁡ZKq,

with ZKq=1 under the empty-space convention. These are exact finite-dimensional Gaussian identities and make no approximation or empirical claim.

Facts & Assumptions

Given: ACω, a finite symmetric positive-definite K0, and a finite nested retained-coordinate list as in the algorithm.

[F1]

For a positive-definite precision matrix K on Rn with n≥1, the density proportional to e−zTKz/2 has normalizer (2π)n/2(det⁡K)−1/2; the empty-space normalizer is 1 by definition (Gaussian covariance for positive quadratic forms and a boundary counterexample, Finite Gaussian probability with a positive precision matrix).

[F2]

Completing the square gives the Schur-complement identity, and for a nonempty retained block its Schur complement is positive definite (Positive Schur complements for finite Gaussian precisions).

Proof

1.1givenF2algebra

Initially K0 is positive definite. At any later stage with nonempty Ej, the current Kj−1 is positive definite by induction, so its principal block Cj is positive definite. The Schur identity in [F2] then gives exactly the algorithm's Kj=Aj−BjCj−1BjT and proves it positive definite when Rj is nonempty. If Rj is empty, the empty-matrix convention applies. Stages with Ej=∅ leave Kj=Kj−1. The algorithm is consequently well-defined at every one of its finitely many stages.

2.1F1F2F3step 1.1algebra

Fix one stage with Ej≠∅ and fix its retained coordinates x. By step 1.1 the current matrix is well-defined and positive definite. Reordering the coordinates is a linear permutation with Jacobian determinant of absolute value 1, so the change-of-variables formula in [F3] preserves the integral. With eliminated coordinates denoted by y, [F2] gives (xy)TKj−1(xy)=xTKjx+(y+Cj−1BjTx)TCj(y+Cj−1BjTx). For fixed x, the translation y↦y+Cj−1BjTx is a C1 diffeomorphism of Euclidean space with Jacobian 1. The change-of-variables formula [F3] and the Gaussian normalizer [F1] therefore give ∫REje−12(x,y)TKj−1(x,y) dy=(2π)∣Ej∣/2det⁡Cje−12xTKjx=eℓje−12xTKjx. The last equality follows from the definition of ℓj and positivity of det⁡Cj. If Ej=∅, the same identity holds with factor 1=eℓj by definition.

3.1F3F4step 2.1algebra

Apply step 2.1 successively for the finite stages. At each stage the integrand is nonnegative, so Tonelli [F3] permits the iterated eliminations in the stated order. Multiplying the one-stage factors yields, for each fixed final retained vector x, ∫R⋃jEje−12z~(x,y)TK0z~(x,y) dy=(∏j=1qeℓj)e−12xTKqx=e∑jℓje−12xTKqx, using exponential addition in [F4]. If ⋃jEj=∅, including q=0, the integral is with respect to the unit mass on R0 and the equality is the identity with an empty product.

4.1F1F3F4step 1.1step 3.1algebra∎

If no coordinates are eliminated, then every Ej=∅, Kq=K0, and ℓj=0, so the normalizer identity is immediate. If Rq=∅, then ⋃jEj=R0 has N≥1 coordinates; evaluating step 3.1 at the unique empty retained vector gives ZK0=e∑jℓj, while ZKq=1. In the remaining case both Rq and ⋃jEj are nonempty, so Tonelli [F3] and the Euclidean product-measure identity in [F3] allow integration of step 3.1 over the retained vector and give ZK0=e∑jℓjZKq. The nonempty Kq is positive definite by step 1.1, so [F1] makes ZKq finite and positive. In each case, dividing step 3.1 by ZK0 gives the retained density ZKq−1e−xTKqx/2 when Rq≠∅, exactly γKq; for empty Rq the marginal is the unit mass γ∅. Taking natural logarithms of the positive normalizer identity and using [F4] proves the logarithmic formula, including the empty-space case.

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