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One-dimensional Prékopa–Leindler inequality on ℝ (lower-integral form)

Proved
ConvexOptimization.prekopa_leindler_real_line

by Yifan Hong · Aug 15, 2026 · Mathlib c5ea003 (Lean v4.30.0)

convexanalysisconvexoptimizationlog-concavitymeasure-theory

Let 0<λ<10 < \lambda < 10<λ<1, and let f,g,h:R→[0,+∞]f,g,h : \mathbb{R} \to [0,+\infty]f,g,h:R→[0,+∞] be measurable. Assume that for every x,y∈Rx,y \in \mathbb{R}x,y∈R,

f(x)1−λg(y)λ≤h((1−λ)x+λy).f(x)^{1-\lambda}g(y)^{\lambda} \le h((1-\lambda)x+\lambda y).f(x)1−λg(y)λ≤h((1−λ)x+λy).

Then their lower Lebesgue integrals satisfy

(∫Rf)1−λ(∫Rg)λ≤∫Rh.\left(\int_{\mathbb{R}} f\right)^{1-\lambda} \left(\int_{\mathbb{R}} g\right)^{\lambda} \le \int_{\mathbb{R}} h.(∫R​f)1−λ(∫R​g)λ≤∫R​h.

This is the canonical real-coordinate form of the one-dimensional Prékopa–Leindler inequality. It isolates the analytic core from any particular finite-dimensional Euclidean-space representation, making it reusable under measure-preserving linear coordinate changes.

Formalization Note Function values and integrals lie in the extended nonnegative reals, so +∞+\infty+∞ is permitted. Scalar multiplication on R\mathbb{R}R expresses the affine interpolation in the same form used by general real modules.

Preamble
import Mathlib

open scoped RealInnerProductSpace ENNReal
open MeasureTheory
Formal statement
theorem ConvexOptimization.prekopa_leindler_real_line
    (l : ℝ) (hl0 : 0 < l) (hl1 : l < 1)
    (f g h : ℝ → ℝ≥0∞)
    (hf : Measurable f) (hg : Measurable g) (hh : Measurable h)
    (hple : ∀ x y : ℝ,
      f x ^ (1 - l) * g y ^ l ≤ h ((1 - l) • x + l • y)) :
    (∫⁻ x, f x) ^ (1 - l) * (∫⁻ x, g x) ^ l ≤ ∫⁻ x, h x := by sorry
Source
Richard J. Gardner, The Brunn-Minkowski Inequality: A Survey with Proofs, https://faculty.gardner.wwu.edu/gorizia12.pdf, Theorem 4.1 (pp. 6–8); András Prékopa, On logarithmic concave measures and functions, Acta Sci. Math. 34 (1973), 335–343, https://rutcor.rutgers.edu/Prekopa/pdf/SCIENT2.pdf, §2, equations (2.1)–(2.2), for the arbitrary-weight extended-integral formulation; András Prékopa, Logarithmic concave measures with application to stochastic programming, Acta Sci. Math. 32 (1971), 301–316, https://acta.bibl.u-szeged.hu/14319/1/math_032_fasc_003_004_301-316.pdf, Theorem 1 (pp. 303–308), for the measurable half-weight case with infinite integrals explicitly permitted.

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