openAI-math-dataset / lean /OAI /Probability /SKBarriers /Calculus /ParameterWeakGrowth.lean
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import Mathlib
import OAI.Probability.SKBarriers.Calculus.ParameterGrowth
namespace OAI
section
section
noncomputable section
open scoped BigOperators
open MeasureTheory ProbabilityTheory Filter
namespace SK.Analytic
section ParameterWeakGrowth
variable {P E F : Type} [NormedAddCommGroup P] [NormedAddCommGroup E] [Norm F]
theorem ParamExpGrowth.weakNorm {f : P × E → F} (hf : ParamExpGrowth f)
(hn : ∀ z, 0 ≤ ‖f z‖) : ParamExpGrowth (fun z => ‖f z‖) := by
apply hf.of_norm_le zero_le_one
intro z
rw [Real.norm_eq_abs,abs_of_nonneg (hn z),one_mul]
theorem ParamExpGrowth.weakLocallyDominated {f : P × (E × ℝ) → F}
(hf : ParamExpGrowth f) :
GaussianLocallyDominated (fun z : (P × E) × ℝ => f (z.1.1,(z.1.2,z.2))) := by
intro R
obtain ⟨C,M,hC,hM,hf⟩ := hf R
refine ⟨fun y => C*Real.exp (M*R)*Real.exp (M*|y|),
(integrable_exp_mul_abs_gaussian M).const_mul _,?_⟩
rintro ⟨p,e⟩ y hpe
have hp : ‖p‖ ≤ R := (le_max_left _ _).trans hpe
have he : ‖e‖ ≤ R := (le_max_right _ _).trans hpe
apply (hf p (e,y) hp).trans
change C*Real.exp (M*‖(e,y)‖) ≤ C*Real.exp (M*R)*Real.exp (M*|y|)
rw [mul_assoc,← Real.exp_add,← mul_add]
apply mul_le_mul_of_nonneg_left _ hC
apply Real.exp_le_exp.2
apply mul_le_mul_of_nonneg_left _ hM
rw [Prod.norm_def,Real.norm_eq_abs]
exact max_le (he.trans (le_add_of_nonneg_right (abs_nonneg _)))
(le_add_of_nonneg_left ((norm_nonneg e).trans he))
end ParameterWeakGrowth
end SK.Analytic
end
end
end
end OAI