Lipschitz Gradient from a Second-Difference Bound
ProvedFunctionalInequalities.Analysis.lipschitzWith_gradient_of_second_difference_le_readablefunctional-inequalitieskls-conjecturekls-modularkls-readablelog-concave
Let be a complete real inner-product normed space, let be convex and differentiable, and let . If for every ,
then the gradient is globally Lipschitz with constant (represented in Lean by Real.toNNReal (4 * C)):
This is the standard consequence of the centered second-difference estimate.
Original Lean declaration header
Within namespace FunctionalInequalities.Analysis. Shown in its original module context. Proof omitted.
theorem lipschitzWith_gradient_of_second_difference_le {E : Type*} [NormedAddCommGroup E]
[InnerProductSpace ℝ E] [CompleteSpace E] {u : E → ℝ} {C : ℝ}
(hconv : ConvexOn ℝ Set.univ u) (hdiff : Differentiable ℝ u) (hC : 0 ≤ C)
(hsd : ∀ x h : E, u (x + h) + u (x - h) - 2 * u x ≤ C * ‖h‖ ^ 2) :
LipschitzWith (Real.toNNReal (4 * C)) (gradient u)
Module navigation
Module: Analysis/Convex/SecondDifference.
Next: sd_penalized_estimate.
Source excerpt, lines 217–221 · Frozen source 408915f.
Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_Convex_SecondDifference
Formal statement
theorem FunctionalInequalities.Analysis.lipschitzWith_gradient_of_second_difference_le_readable {E : Type*} [NormedAddCommGroup E]
[InnerProductSpace ℝ E] [CompleteSpace E] {u : E → ℝ} {C : ℝ}
(hconv : ConvexOn ℝ Set.univ u) (hdiff : Differentiable ℝ u) (hC : 0 ≤ C)
(hsd : ∀ x h : E, u (x + h) + u (x - h) - 2 * u x ≤ C * ‖h‖ ^ 2) :
LipschitzWith (Real.toNNReal (4 * C)) (gradient u) := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/Convex/SecondDifference.lean#L215-L480 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.Convex.SecondDifference`, declaration `FunctionalInequalities.Analysis.lipschitzWith_gradient_of_second_difference_le`; the repository is private at upload time).