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Partitioned inverse, off-diagonal row entries: ((GG⊤)−1)i,j=−((HH⊤)−1Hg)j ((GG⊤)−1)ii((GG^\top)^{-1})_{i,j} = -\big((HH^\top)^{-1}Hg\big)_{j}\,((GG^\top)^{-1})_{ii}((GG⊤)−1)i,j​=−((HH⊤)−1Hg)j​((GG⊤)−1)ii​

Proved
GaussianMatrix.schur_offdiag_inv

by tc · Oct 9, 2026 · Mathlib 0df444a (Lean v4.33.1)

linear-algebramatrix-inverserandom-matricesschur-complement

Let G∈R(n+1)×kG \in \mathbb{R}^{(n+1)\times k}G∈R(n+1)×k, fix a row index iii, let g=Gi,:g = G_{i,:}g=Gi,:​ be the iii-th row and H∈Rn×kH \in \mathbb{R}^{n\times k}H∈Rn×k the matrix of the remaining rows in their original order, and assume HH⊤HH^\topHH⊤ is invertible. Let c=(HH⊤)−1Hg∈Rnc = (HH^\top)^{-1}Hg \in \mathbb{R}^nc=(HH⊤)−1Hg∈Rn be the least-squares coefficients of ggg on the rows of HHH. Then for every a∈{1,…,n}a \in \{1,\dots,n\}a∈{1,…,n}, writing i⊕ai \oplus ai⊕a for the index in GGG of the aaa-th row of HHH,

((GG⊤)−1)i, i⊕a  =  − ca ((GG⊤)−1)ii.\big((GG^\top)^{-1}\big)_{i,\,i\oplus a} \;=\; -\,c_a\,\big((GG^\top)^{-1}\big)_{ii}.((GG⊤)−1)i,i⊕a​=−ca​((GG⊤)−1)ii​.

Together with the diagonal formula ((GG⊤)−1)ii=1/(g⊤g−(Hg)⊤(HH⊤)−1Hg)((GG^\top)^{-1})_{ii} = 1/(g^\top g - (Hg)^\top(HH^\top)^{-1}Hg)((GG⊤)−1)ii​=1/(g⊤g−(Hg)⊤(HH⊤)−1Hg) (schur_diag_inv), this describes the whole iii-th row of (GG⊤)−1(GG^\top)^{-1}(GG⊤)−1: it equals Q−1(1,−c⊤)Q^{-1}(1, -c^\top)Q−1(1,−c⊤) (in the ordering iii first), with QQQ the Schur complement. It is the deterministic step that turns off-diagonal entries of an inverse Wishart matrix into a ratio of independent quantities (regression coefficients over a residual).

Formalization Note. The index i⊕ai\oplus ai⊕a is i.succAbove a and HHH is G.submatrix i.succAbove id. Lean's matrix inverse is total; the identity holds under the sole hypothesis det⁡(HH⊤)≠0\det(HH^\top)\ne 0det(HH⊤)=0, including the degenerate case Q=0Q = 0Q=0, where GG⊤GG^\topGG⊤ is singular and both sides are 000.

Preamble
import Definitions.Def_GaussianMatrix_basic

open MeasureTheory ProbabilityTheory
open scoped Matrix
Formal statement
namespace GaussianMatrix

theorem schur_offdiag_inv {n k : ℕ} (G : Matrix (Fin (n + 1)) (Fin k) ℝ) (i : Fin (n + 1))
    (a : Fin n)
    (hH : (G.submatrix i.succAbove id * (G.submatrix i.succAbove id)ᵀ).det ≠ 0) :
    (G * Gᵀ)⁻¹ i (i.succAbove a) =
      -(((G.submatrix i.succAbove id * (G.submatrix i.succAbove id)ᵀ)⁻¹ *ᵥ
          (G.submatrix i.succAbove id *ᵥ G i)) a) * (G * Gᵀ)⁻¹ i i := by sorry

end GaussianMatrix
Source
standard fact (inverse of a partitioned matrix): for M=(ab⊤bC)M=\begin{pmatrix} a & b^\top \\ b & C\end{pmatrix}M=(ab​b⊤C​) with CCC invertible and s=a−b⊤C−1b≠0s = a - b^\top C^{-1}b \ne 0s=a−b⊤C−1b=0, the first row of M−1M^{-1}M−1 is s−1 (1, −b⊤C−1)s^{-1}\,(1,\,-b^\top C^{-1})s−1(1,−b⊤C−1); R. A. Horn and C. R. Johnson, Matrix Analysis, 2nd ed., Cambridge Univ. Press 2013, Section 0.7.3.

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