Log-convexity of the mixed integral along a denominator ray
ProvedRybinAI2026.P01.crossIntegral_add_logConvexintegral-inequalitylog-convexitymatrix-analysispositive-definite-matrices
Let be real square matrices of the same size, and assume that are symmetric positive definite. For a numerator , write for the mission's mixed double spherical integral with positive-definite quadratic denominators and . Then
and, symmetrically in the second sphere variable,
Thus the mixed integral is midpoint log-convex when either positive quadratic denominator moves along the ray . This supplies a reusable square-contraction step for reductions of the matrix-integral addition problem.
Formalization Note The Lean expression ((A+B)+B) represents . The theorem retains the original unnormalized spherical surface measure and also covers the zero-dimensional case.
Preamble
import Definitions.Def_rybin2026_p01_cross_integral set_option autoImplicit false open Matrix RybinAI2026.P01
Formal statement
theorem RybinAI2026.P01.crossIntegral_add_logConvex
{n : ℕ} (X Y A B C : Matrix (Fin n) (Fin n) ℝ)
(hA : A.PosDef) (hB : B.PosDef) (hC : C.PosDef) :
(crossIntegral X Y (A+B) C)^2 ≤
crossIntegral X Y A C*crossIntegral X Y ((A+B)+B) C ∧
(crossIntegral X Y C (A+B))^2 ≤
crossIntegral X Y C A*crossIntegral X Y C ((A+B)+B) := by
sorrySource
Derived denominator log-convexity lemma for CUHK-Shenzhen AI Math Problems, Problem 1 (Prof. Cosme Louart), https://rybindmitry.github.io/problems/1.html; the source supplies the mixed spherical kernel, while this reusable consequence is proved directly from that definition.