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Atlas.ProjectionTheory.code.EpsilonImprovement

Abstract data packaging the projective AD-regular projection configurations indexed by parameters s, t and scale δ, together with a nonnegative, uniformly bounded "ratio" functional whose supremum defines R_{AD, proj}(s, t, δ).

Instances For

    The canonical ProjConfigDataST instance used to build the projective AD-regular projection quantity R_{AD, proj}(s, t, δ).

    Instances For
      noncomputable def ProjectionTheory.R_AD_proj_st (s t δ : ℝ) :

      The projective AD-regular projection quantity R_{AD, proj}(s, t, δ), defined as the supremum of the ratio over all admissible configurations at parameters (s, t, δ).

      Instances For
        theorem ProjectionTheory.R_AD_proj_epsilon_improvement_submult (s t : ℝ) (hs : 0 < s) (ht : 0 < t) (ht' : t < 2) (α : ℝ) (hα : 0 < α) :
        ∃ (ε : ℝ), 0 < ε ∧ ∃ (C : ℝ), 0 < C ∧ ∀ (δ : ℝ), 0 < δ → δ < 1 → R_AD_proj_st s t (δ ^ (1 / 2)) ≤ C * δ ^ (-α) * max 1 (max (δ ^ (-(t / 2)) * δ ^ (s / 2)) (δ ^ (1 - t))) ∨ R_AD_proj_st s t δ ≤ C * δ ^ ε * R_AD_proj_st s t (δ ^ (1 / 2)) ^ 2

        Lemma (ε-improvement to the projective submultiplicative lemma). Fix s, t with 0 < s and 0 < t < 2. For every α > 0 there exist ε > 0 and C > 0 such that for every scale δ ∈ (0, 1), either R_{AD, proj}(δ^{1/2}) ≲ δ^{-α} · max(1, δ^{-t/2+s/2}, δ^{1-t}) (the desired bound already holds at scale δ^{1/2}), or the submultiplicative inequality improves to R_{AD, proj}(δ) ≲ δ^{ε} · R_{AD, proj}(δ^{1/2})².