Samplinglib
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Read the statements and derivations in source order. Every result has its own optional Lean statement and proof. Source assumptions, library reuse and unproved boundaries are kept explicit.

  1. Construct the fixed smooth unit cutoff
  2. Evaluate the unit cutoff through the smooth transition
  3. The unit cutoff is infinitely differentiable
  4. The unit cutoff equals one on the closed unit interval
  5. The unit cutoff vanishes at and beyond radius two
  6. Every unit-cutoff value lies between zero and one
  7. The unit cutoff has compact closed support
  8. A fixed positive constant bounds the first derivative of the unit cutoff
  9. The second scalar derivative of the unit cutoff is continuous
  10. The second scalar derivative has compact support
  11. A fixed positive constant bounds the second scalar derivative
  12. The radial cutoff at a real scale
  13. The radial cutoff equals one on the inner closed ball
  14. The radial cutoff vanishes on and beyond the outer sphere
  15. Every radial cutoff value lies between zero and one
  16. A totalized derivative bound for the scaled norm
  17. Positive-radius radial cutoffs are smooth
  18. One constant controls every positive-radius first derivative
  19. The totalized derivative vanishes throughout the outer zero region
  20. The ordinary support lies in the outer closed ball
  21. The closed support lies in the same outer closed ball
  22. Finite dimension makes the radial cutoff compactly supported
  23. An existing uniform second-derivative bound under dilation
  24. At each fixed point the cutoffs are eventually equal to one
  25. A smooth compactly supported plateau inside any open neighborhood of a compact set
ASTIS mathematical exposition

Construct the fixed smooth unit cutoff

AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff · def · Teaching coverage

Statement

Define q:ℝ→ℝ to be Mathlib's inner-product bump base on the real line with outer-radius parameter 2. This is a fixed function, before any radial scale is chosen; its pointwise description is q(t)=S(2−|t|), where S is the smooth transition from 0 to 1.

\[q(t):=B_{\mathbb R}(2,t),\qquad B_E(\rho,x)=S\!\left(\frac{\rho-\|x\|}{\rho-1}\right),\qquad q(t)=S(2-|t|).\]

All objects and hypotheses

  • There are no explicit parameters: q is a fixed real-to-real function.

Construction and meaning

1. Use the existing smooth bump family

Mathlib's inner-product-space bump base supplies a family B_E(ρ,x), built from the smooth transition S and the norm. Select E=ℝ.

\[B_{\mathbb R}(\rho,t)=S\!\left(\frac{\rho-|t|}{\rho-1}\right).\]
Corresponding Lean step

ContDiffBumpBase.ofInnerProductSpace ℝ

2. Fix its scale parameter at two

Evaluate the family at ρ=2. The denominator becomes 1, which yields the transition expression proved formally in the next declaration.

\[q(t)=B_{\mathbb R}(2,t)=S(2-|t|).\]
Corresponding Lean step

(ContDiffBumpBase.ofInnerProductSpace ℝ).toFun 2

Lean statement · smoothUnitCutoff

noncomputable marks an abstract real function construction. The one-dimensional q is distinct from the later family χ_R on an arbitrary normed space.

Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

noncomputable def smoothUnitCutoff : ℝ → ℝ

Exact module and namespace context

Lean construction · smoothUnitCutoff

Construction, not a theorem proof. Use the existing smooth bump family: This is an imported structure containing the family and its properties; no smooth-bump existence theorem is reproved. Fix its scale parameter at two: toFun extracts the function from the bump structure. This body constructs q; it is not a theorem proof.

Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

noncomputable def smoothUnitCutoff : ℝ → ℝ :=
  (ContDiffBumpBase.ofInnerProductSpace ℝ).toFun 2

/-- The unit cutoff written using Mathlib's smooth transition function. -/

Exact module and namespace context

Scope and omitted-condition boundaries

  • Construction only; later declarations expose smoothness, values, support and derivative bounds.

Source and reuse

ASTIS parents called

    Mathlib API called (external library)

    • ContDiffBumpBase.ofInnerProductSpace

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    Evaluate the unit cutoff through the smooth transition

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff_eq_smoothTransition · theorem · Teaching coverage

    Statement

    For every real t, the fixed unit cutoff is q(t)=S(2−|t|), where S=Real.smoothTransition.

    \[\forall t\in\mathbb R,\quad q(t)=S(2-|t|).\]

    All objects and hypotheses

    • x : ℝ is arbitrary (called t in the display).

    Mathematical proof

    1. Unfold the bump-base formula

    The selected bump base evaluates S at (2−‖t‖)/(2−1).

    \[q(t)=S\!\left(\frac{2-\|t\|}{2-1}\right).\]
    Corresponding Lean step

    simp [smoothUnitCutoff, ContDiffBumpBase.ofInnerProductSpace, Real.norm_eq_abs, hden]

    2. Simplify the real norm and denominator

    On ℝ, ‖t‖=|t| and 2−1=1. Substitution leaves exactly S(2−|t|).

    \[\|t\|=|t|,\quad 2-1=1,\quad (2-|t|)/1=2-|t|.\]
    Corresponding Lean step

    have hden : (2 : ℝ) - 1 = 1 := by norm_num

    Lean statement · smoothUnitCutoff_eq_smoothTransition

    This is an equality of functions evaluated at an arbitrary input. It adds an accessible formula for the existing definition, not a second cutoff.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_eq_smoothTransition (x : ℝ) :
        smoothUnitCutoff x = Real.smoothTransition (2 - |x|)

    Exact module and namespace context

    Lean proof · smoothUnitCutoff_eq_smoothTransition

    Actual proof outline: Unfold the bump-base formula: The rewrite list uses the two actual definitions and the identity of the real norm with absolute value. Simplify the real norm and denominator: hden is a proved arithmetic equality, not an extra hypothesis; the final simp applies it.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_eq_smoothTransition (x : ℝ) :
        smoothUnitCutoff x = Real.smoothTransition (2 - |x|) := by
      have hden : (2 : ℝ) - 1 = 1 := by norm_num
      simp [smoothUnitCutoff, ContDiffBumpBase.ofInnerProductSpace, Real.norm_eq_abs, hden]
    
    /-- The unit cutoff is infinitely differentiable. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • Do not infer smoothness merely by treating |t| as differentiable at zero; smoothness is established separately.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • ContDiffBumpBase.ofInnerProductSpace
    • Real.norm_eq_abs

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    The unit cutoff is infinitely differentiable

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff_contDiff · theorem · Teaching coverage

    Statement

    The fixed function q:ℝ→ℝ is C∞ on the whole real line, including t=0.

    \[q\in C^\infty(\mathbb R;\mathbb R).\]

    All objects and hypotheses

    • No parameters or additional hypotheses.

    Mathematical proof

    1. Use joint smoothness in the valid bump-parameter region

    The bump base is jointly smooth on (1,∞)×ℝ. For any t, the point (2,t) belongs to this open set, so it has a neighborhood on which the joint function is smooth.

    \[(2,t)\in(1,\infty)\times\mathbb R,\qquad B_{\mathbb R}\text{ is }C^\infty\text{ near }(2,t).\]
    Corresponding Lean step
    have hmem := ⟨by norm_num, Set.mem_univ x⟩
    have hnhds := (isOpen_Ioi.prod isOpen_univ).mem_nhds hmem
    have hbase := (ContDiffBumpBase.ofInnerProductSpace ℝ).smooth.contDiffAt hnhds
    2. Insert the fixed parameter smoothly

    The map t↦(2,t) is smooth because its coordinates are a constant and the identity.

    \[J(t)=(2,t),\qquad J\in C^\infty.\]
    Corresponding Lean step

    have hpair := (contDiffAt_const (c := (2 : ℝ))).prodMk contDiffAt_id

    3. Compose and conclude at every point

    The cutoff is Bℝ∘J. Compose the two smooth maps at each t, then use the pointwise characterization of global C∞ regularity.

    \[q=B_{\mathbb R}\circ J\in C^\infty.\]
    Corresponding Lean step
    rw [contDiff_iff_contDiffAt]
    intro x
    -- after hbase and hpair:
    exact hbase.comp x hpair
    Lean statement · smoothUnitCutoff_contDiff

    ContDiff ℝ (⊤ : ℕ∞) is the C∞ assertion. Smoothness comes from the bump family, whose plateau resolves the apparent absolute-value singularity.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_contDiff : ContDiff ℝ (⊤ : ℕ∞) smoothUnitCutoff

    Exact module and namespace context

    Lean proof · smoothUnitCutoff_contDiff

    Actual proof outline: Use joint smoothness in the valid bump-parameter region: hnhds turns the open-domain membership into the neighborhood condition needed to pass from ContDiffOn to ContDiffAt. Insert the fixed parameter smoothly: prodMk pairs two smooth component functions. Compose and conclude at every point: The pointwise proof is repeated for arbitrary x. It does not assume that the norm itself is smooth at the origin.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_contDiff : ContDiff ℝ (⊤ : ℕ∞) smoothUnitCutoff := by
      rw [contDiff_iff_contDiffAt]
      intro x
      have hmem :
          ((2 : ℝ), x) ∈ (Set.Ioi (1 : ℝ) ×ˢ (Set.univ : Set ℝ)) := by
        exact ⟨by norm_num, Set.mem_univ x⟩
      have hnhds :
          (Set.Ioi (1 : ℝ) ×ˢ (Set.univ : Set ℝ)) ∈ 𝓝 ((2 : ℝ), x) := by
        exact (isOpen_Ioi.prod isOpen_univ).mem_nhds hmem
      have hbase :
          ContDiffAt ℝ (⊤ : ℕ∞)
            (Function.uncurry (ContDiffBumpBase.ofInnerProductSpace ℝ).toFun)
            ((2 : ℝ), x) :=
        (ContDiffBumpBase.ofInnerProductSpace ℝ).smooth.contDiffAt hnhds
      have hpair : ContDiffAt ℝ (⊤ : ℕ∞) (fun y : ℝ => ((2 : ℝ), y)) x :=
        (contDiffAt_const (c := (2 : ℝ))).prodMk contDiffAt_id
      change ContDiffAt ℝ (⊤ : ℕ∞)
        (fun y : ℝ =>
          Function.uncurry (ContDiffBumpBase.ofInnerProductSpace ℝ).toFun ((2 : ℝ), y)) x
      exact hbase.comp x hpair
    
    /-- The unit cutoff is one when `|x| <= 1`. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • Global one-dimensional smoothness; no radial-space or radius assumptions occur yet.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • contDiff_iff_contDiffAt
    • ContDiffBumpBase.ofInnerProductSpace
    • ContDiffOn.contDiffAt
    • ContDiffAt.prodMk
    • ContDiffAt.comp
    • contDiffAt_const
    • contDiffAt_id

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    The unit cutoff equals one on the closed unit interval

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff_eq_one_of_abs_le_one · theorem · Teaching coverage

    Statement

    For any real t satisfying |t|≤1, q(t)=1, including both endpoints t=−1 and t=1.

    \[|t|\le1\Longrightarrow q(t)=1.\]

    All objects and hypotheses

    • x : ℝ with hx : |x|≤1.

    Mathematical proof

    1. Put the transition input in its upper plateau

    The hypothesis gives 2−|t|≥1.

    \[|t|\le1\Longrightarrow 1\le2-|t|.\]
    Corresponding Lean step

    have h : (1 : ℝ) ≤ 2 - |x| := by linarith

    2. Use the transition's value-one theorem

    S(u)=1 whenever u≥1. Apply this at u=2−|t| and rewrite q using its evaluation formula.

    \[q(t)=S(2-|t|)=1.\]
    Corresponding Lean step

    simpa [smoothUnitCutoff_eq_smoothTransition] using (Real.smoothTransition.one_of_one_le (x := 2 - |x|) h)

    Lean statement · smoothUnitCutoff_eq_one_of_abs_le_one

    The implicit x is a real input and hx is the exact closed-interval premise.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_eq_one_of_abs_le_one {x : ℝ} (hx : |x| ≤ 1) :
        smoothUnitCutoff x = 1

    Exact module and namespace context

    Lean proof · smoothUnitCutoff_eq_one_of_abs_le_one

    Actual proof outline: Put the transition input in its upper plateau: The linear arithmetic step is exactly the rearrangement of hx. Use the transition's value-one theorem: one_of_one_le includes equality at the endpoint, so this is a closed-interval assertion.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_eq_one_of_abs_le_one {x : ℝ} (hx : |x| ≤ 1) :
        smoothUnitCutoff x = 1 := by
      have h : (1 : ℝ) ≤ 2 - |x| := by linarith
      simpa [smoothUnitCutoff_eq_smoothTransition] using
        (Real.smoothTransition.one_of_one_le (x := 2 - |x|) h)
    
    /-- The unit cutoff vanishes when `2 <= |x|`. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • Only a value identity is asserted; no derivative formula is added here.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • Real.smoothTransition.one_of_one_le

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    The unit cutoff vanishes at and beyond radius two

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff_eq_zero_of_two_le_abs · theorem · Teaching coverage

    Statement

    For any real t with 2≤|t|, q(t)=0. The boundary points ±2 are included.

    \[2\le|t|\Longrightarrow q(t)=0.\]

    All objects and hypotheses

    • x : ℝ with hx : 2≤|x|.

    Mathematical proof

    1. Put the transition input in its zero region

    The hypothesis gives 2−|t|≤0.

    \[2\le|t|\Longrightarrow 2-|t|\le0.\]
    Corresponding Lean step

    have h : 2 - |x| ≤ 0 := by linarith

    2. Apply the zero-value rule

    S(u)=0 for u≤0. The unit-cutoff evaluation formula therefore gives q(t)=0.

    \[q(t)=S(2-|t|)=0.\]
    Corresponding Lean step

    simpa [smoothUnitCutoff_eq_smoothTransition] using (Real.smoothTransition.zero_of_nonpos (x := 2 - |x|) h)

    Lean statement · smoothUnitCutoff_eq_zero_of_two_le_abs

    The non-strict inequality is important: the function vanishes on the boundary itself.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_eq_zero_of_two_le_abs {x : ℝ} (hx : 2 ≤ |x|) :
        smoothUnitCutoff x = 0

    Exact module and namespace context

    Lean proof · smoothUnitCutoff_eq_zero_of_two_le_abs

    Actual proof outline: Put the transition input in its zero region: h is the elementary consequence of hx needed by the transition API. Apply the zero-value rule: zero_of_nonpos is an imported value theorem, not a support or differentiability assumption.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_eq_zero_of_two_le_abs {x : ℝ} (hx : 2 ≤ |x|) :
        smoothUnitCutoff x = 0 := by
      have h : 2 - |x| ≤ 0 := by linarith
      simpa [smoothUnitCutoff_eq_smoothTransition] using
        (Real.smoothTransition.zero_of_nonpos (x := 2 - |x|) h)
    
    /-- The unit cutoff takes values in `[0, 1]`. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • Vanishing at a boundary point must not be confused with being locally constant there.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • Real.smoothTransition.zero_of_nonpos

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    Every unit-cutoff value lies between zero and one

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff_mem_Icc · theorem · Teaching coverage

    Statement

    For every real t, 0≤q(t)≤1.

    \[\forall t\in\mathbb R,\qquad q(t)\in[0,1].\]

    All objects and hypotheses

    • x : ℝ arbitrary.

    Mathematical proof

    1. Evaluate the bump family's range guarantee

    The bump base already includes the [0,1] range property at every parameter and input. Apply it to parameter 2 and t.

    \[B_{\mathbb R}(2,t)\in[0,1].\]
    Corresponding Lean step

    exact (ContDiffBumpBase.ofInnerProductSpace ℝ).mem_Icc 2 x

    Lean statement · smoothUnitCutoff_mem_Icc

    Icc 0 1 is the closed interval, so both endpoints are allowed.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_mem_Icc (x : ℝ) : smoothUnitCutoff x ∈ Set.Icc (0 : ℝ) 1

    Exact module and namespace context

    Lean proof · smoothUnitCutoff_mem_Icc

    Actual proof outline: Evaluate the bump family's range guarantee: mem_Icc is the range field of the imported bump structure. Its two components are the lower and upper inequalities.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_mem_Icc (x : ℝ) : smoothUnitCutoff x ∈ Set.Icc (0 : ℝ) 1 := by
      exact (ContDiffBumpBase.ofInnerProductSpace ℝ).mem_Icc 2 x
    
    /-- The one-dimensional unit cutoff has compact support. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • The theorem supplies bounds, not strict positivity throughout the transition region.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • ContDiffBumpBase.ofInnerProductSpace

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    The unit cutoff has compact closed support

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff_hasCompactSupport · theorem · Teaching coverage

    Statement

    The topological support of q—the closure of {t:q(t)≠0}—is compact in ℝ.

    \[\operatorname{supp}(q)\subseteq[-2,2],\qquad\operatorname{tsupp}(q)=\overline{\operatorname{supp}(q)}\text{ compact}.\]

    All objects and hypotheses

    • No explicit parameter or additional hypothesis; the ambient space is ℝ.

    Mathematical proof

    1. Choose a compact enclosure

    The interval [−2,2] is compact. It suffices to place the ordinary nonzero support inside it.

    \[K=[-2,2]\text{ is compact}.\]
    Corresponding Lean step

    apply HasCompactSupport.of_support_subset_isCompact (K := Set.Icc (-2 : ℝ) 2) isCompact_Icc

    2. Exclude nonzero values outside the interval

    If q(t)≠0 but t<−2 or t>2, then |t|≥2, so the vanishing theorem gives q(t)=0, a contradiction. Thus every support point lies in the chosen interval.

    \[q(t)\ne0\Longrightarrow -2\le t\le2.\]
    Corresponding Lean step
    rw [Function.mem_support] at hx
    -- The two contradiction branches use smoothUnitCutoff_eq_zero_of_two_le_abs,
    -- with abs_of_nonpos on the left and abs_of_nonneg on the right.
    Lean statement · smoothUnitCutoff_hasCompactSupport

    HasCompactSupport concerns the closed topological support, not merely whether the nonzero set itself is compact.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_hasCompactSupport : HasCompactSupport smoothUnitCutoff

    Exact module and namespace context

    Lean proof · smoothUnitCutoff_hasCompactSupport

    Actual proof outline: Choose a compact enclosure: The helper converts containment of ordinary support in a compact set into compactness of its closure. Exclude nonzero values outside the interval: Support membership means nonzero value. The proof treats the two interval inequalities separately, using the sign-appropriate absolute-value identity.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_hasCompactSupport : HasCompactSupport smoothUnitCutoff := by
      apply HasCompactSupport.of_support_subset_isCompact
        (K := Set.Icc (-2 : ℝ) 2) isCompact_Icc
      intro x hx
      rw [Function.mem_support] at hx
      simp only [Set.mem_Icc]
      constructor
      · by_contra hleft
        push Not at hleft
        apply hx
        apply smoothUnitCutoff_eq_zero_of_two_le_abs
        rw [abs_of_nonpos (by linarith : x ≤ 0)]
        linarith
      · by_contra hright
        push Not at hright
        apply hx
        apply smoothUnitCutoff_eq_zero_of_two_le_abs
        rw [abs_of_nonneg (by linarith : 0 ≤ x)]
        linarith
    
    /-- The derivative of the one-dimensional unit cutoff is bounded by one
    positive constant.  The constant is chosen before any radial scale. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • The source proves containment and compactness, not equality of topological support with a particular interval.
    • No direct named invocation was found in the inspected cutoff example block Tests/Basic.lean:49–169; this is not a statement about all possible project consumers.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • HasCompactSupport.of_support_subset_isCompact
    • isCompact_Icc
    • Function.mem_support
    • abs_of_nonpos
    • abs_of_nonneg

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    A fixed positive constant bounds the first derivative of the unit cutoff

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff_deriv_bounded · theorem · Teaching coverage

    Statement

    There exists C>0 such that |q′(t)|≤C for every real t. This constant is chosen before any radial scale.

    \[\exists C>0\ \forall t\in\mathbb R,\quad|q'(t)|\le C.\]

    All objects and hypotheses

    • No radius R occurs in this statement. The derivative is the scalar real derivative of the fixed q.

    Mathematical proof

    1. Use smoothness to make q′ continuous

    Since q is C∞, it is at least C¹ and its first derivative is continuous on ℝ.

    \[q\in C^\infty\Longrightarrow q'\text{ continuous}.\]
    Corresponding Lean step

    have hcont := smoothUnitCutoff_contDiff.continuous_deriv (WithTop.coe_le_coe.mpr (le_top : (1 : ℕ∞) ≤ ⊤))

    2. Use compact support to bound the derivative

    A derivative vanishes off the closed support of its original function. Thus q′ has compact support, and its continuity gives a global norm bound B.

    \[\operatorname{tsupp}(q')\subseteq\operatorname{tsupp}(q),\qquad\exists B\ \forall t,\ |q'(t)|\le B.\]
    Corresponding Lean step

    obtain ⟨C, hC⟩ := smoothUnitCutoff_hasCompactSupport.deriv.exists_bound_of_continuous hcont

    3. Make the bound strictly positive

    Replace B by max(B,1). This number is positive and no smaller than B, so it keeps the derivative bound.

    \[C=\max(B,1)>0,\qquad |q'(t)|\le B\le C.\]
    Corresponding Lean step

    exact ⟨max C 1, lt_max_of_lt_right one_pos, fun x => (hC x).trans (le_max_left C 1)⟩

    Lean statement · smoothUnitCutoff_deriv_bounded

    The order ∃C then ∀t is a genuine uniform bound. No numerical value for C is computed.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_deriv_bounded :
        ∃ C : ℝ, 0 < C ∧ ∀ x : ℝ, ‖deriv smoothUnitCutoff x‖ ≤ C

    Exact module and namespace context

    Lean proof · smoothUnitCutoff_deriv_bounded

    Actual proof outline: Use smoothness to make q′ continuous: The order proof says one derivative is available from infinite smoothness. Use compact support to bound the derivative: .deriv transfers compact support, and exists_bound_of_continuous supplies the uniform bound. Make the bound strictly positive: The source's C from the intermediate bound is renamed B here to distinguish it from the final positive witness.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_deriv_bounded :
        ∃ C : ℝ, 0 < C ∧ ∀ x : ℝ, ‖deriv smoothUnitCutoff x‖ ≤ C := by
      have hcont : Continuous (deriv smoothUnitCutoff) :=
        smoothUnitCutoff_contDiff.continuous_deriv
          (WithTop.coe_le_coe.mpr (le_top : (1 : ℕ∞) ≤ ⊤))
      obtain ⟨C, hC⟩ :=
        smoothUnitCutoff_hasCompactSupport.deriv.exists_bound_of_continuous hcont
      exact ⟨max C 1, lt_max_of_lt_right one_pos, fun x =>
        (hC x).trans (le_max_left C 1)⟩
    
    /-- The second derivative of the unit cutoff is continuous. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • This is the first-order input to the radial C/R theorem; no second-order API is needed.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • ContDiff.continuous_deriv
    • HasCompactSupport.deriv
    • HasCompactSupport.exists_bound_of_continuous
    • lt_max_of_lt_right
    • le_max_left

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    The second scalar derivative of the unit cutoff is continuous

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff_secondDeriv_continuous · theorem · Teaching coverage

    Statement

    The function t↦q″(t), defined as deriv(deriv q), is continuous on ℝ.

    \[q''=(q')'\in C^0(\mathbb R;\mathbb R).\]

    All objects and hypotheses

    • No extra hypothesis; q is the existing smooth scalar cutoff.

    Mathematical proof

    1. Retain two derivatives from infinite smoothness

    C∞ regularity implies C² regularity.

    \[q\in C^\infty\Longrightarrow q\in C^2.\]
    Corresponding Lean step

    have htwo : ContDiff ℝ 2 smoothUnitCutoff := smoothUnitCutoff_contDiff.of_le (WithTop.coe_le_coe.mpr (le_top : (2 : ℕ∞) ≤ ⊤))

    2. Differentiate twice and read continuity

    The iterated-derivative rule sends a C² scalar function to a C⁰ function after two derivatives. The second iterate of deriv is exactly deriv(deriv q), and C⁰ implies continuity.

    \[\operatorname{deriv}^{[2]}q=q'',\qquad q''\in C^0.\]
    Corresponding Lean step
    have hzero := htwo.iterate_deriv' 0 2
    simp only [Function.iterate_succ, Function.iterate_zero, Function.comp_apply] at hzero
    exact hzero.continuous
    Lean statement · smoothUnitCutoff_secondDeriv_continuous

    This is the already-existing scalar second-derivative result, not a new radial Hessian statement.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_secondDeriv_continuous :
        Continuous (deriv (deriv smoothUnitCutoff))

    Exact module and namespace context

    Lean proof · smoothUnitCutoff_secondDeriv_continuous

    Actual proof outline: Retain two derivatives from infinite smoothness: of_le weakens the regularity order, not the statement's domain. Differentiate twice and read continuity: deriv^[2] iterates the derivative-taking operation twice; the simplification expands that iteration.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_secondDeriv_continuous :
        Continuous (deriv (deriv smoothUnitCutoff)) := by
      have htwo : ContDiff ℝ 2 smoothUnitCutoff :=
        smoothUnitCutoff_contDiff.of_le
          (WithTop.coe_le_coe.mpr (le_top : (2 : ℕ∞) ≤ ⊤))
      have hzero : ContDiff ℝ 0 (deriv^[2] smoothUnitCutoff) :=
        htwo.iterate_deriv' 0 2
      simp only [Function.iterate_succ, Function.iterate_zero, Function.comp_apply] at hzero
      exact hzero.continuous
    
    /-- The second derivative of the unit cutoff has compact support. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • It asserts continuity only; boundedness and support are distinct following declarations.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • ContDiff.of_le
    • ContDiff.iterate_deriv'
    • ContDiff.continuous

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    The second scalar derivative has compact support

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff_secondDeriv_hasCompactSupport · theorem · Teaching coverage

    Statement

    The closed topological support of q″=deriv(deriv q) is compact in ℝ.

    \[\operatorname{tsupp}(q'')\subseteq\operatorname{tsupp}(q')\subseteq\operatorname{tsupp}(q),\qquad q''\text{ has compact support}.\]

    All objects and hypotheses

    • No additional parameter or hypothesis.

    Mathematical proof

    1. Transfer compact support through both derivatives

    Each scalar differentiation preserves compact support because the derivative is zero where the original function is locally zero. Apply this existing rule first to q, then to q′.

    \[q\text{ compactly supported}\Longrightarrow q'\text{ compactly supported}\Longrightarrow q''\text{ compactly supported}.\]
    Corresponding Lean step

    smoothUnitCutoff_hasCompactSupport.deriv.deriv

    Lean statement · smoothUnitCutoff_secondDeriv_hasCompactSupport

    This is a support-closure reuse theorem; it is separate from continuity of q″.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_secondDeriv_hasCompactSupport :
        HasCompactSupport (deriv (deriv smoothUnitCutoff))

    Exact module and namespace context

    Lean proof · smoothUnitCutoff_secondDeriv_hasCompactSupport

    Actual proof outline: Transfer compact support through both derivatives: The two .deriv calls apply the same Mathlib support theorem at successive derivative orders.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_secondDeriv_hasCompactSupport :
        HasCompactSupport (deriv (deriv smoothUnitCutoff)) :=
      smoothUnitCutoff_hasCompactSupport.deriv.deriv
    
    /-- One positive constant bounds the second derivative of the fixed unit
    cutoff.  The constant is chosen before any radial scale, which is the compact
    support input needed for a later `C / R^2` radial Hessian bound. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • No radius or radial second-derivative bound is proved by this declaration.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • HasCompactSupport.deriv

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    A fixed positive constant bounds the second scalar derivative

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.smoothUnitCutoff_secondDeriv_bounded · theorem · Teaching coverage

    Statement

    There exists C>0 such that |q″(t)|≤C for every real t, before any radial scale is introduced.

    \[\exists C>0\ \forall t\in\mathbb R,\quad|q''(t)|\le C.\]

    All objects and hypotheses

    • The function is the fixed scalar q; no R or ambient radial space occurs.

    Mathematical proof

    1. Bound the continuous compactly supported second derivative

    The preceding two theorems show q″ is continuous and compactly supported. The compact-support boundedness theorem therefore gives some global bound B.

    \[\exists B\ \forall t,\quad |q''(t)|\le B.\]
    Corresponding Lean step

    obtain ⟨C, hC⟩ := smoothUnitCutoff_secondDeriv_hasCompactSupport.exists_bound_of_continuous smoothUnitCutoff_secondDeriv_continuous

    2. Choose a strictly positive witness

    As for the first derivative, enlarge the bound to max(B,1).

    \[C=\max(B,1)>0,\qquad |q''(t)|\le C.\]
    Corresponding Lean step

    exact ⟨max C 1, lt_max_of_lt_right one_pos, fun x => (hC x).trans (le_max_left C 1)⟩

    Lean statement · smoothUnitCutoff_secondDeriv_bounded

    This is an existing scalar bound. The later radial iterated-second-derivative proof actually bounds the unit radial function directly; it does not call this scalar bound.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_secondDeriv_bounded :
        ∃ C : ℝ, 0 < C ∧ ∀ x : ℝ, ‖deriv (deriv smoothUnitCutoff) x‖ ≤ C

    Exact module and namespace context

    Lean proof · smoothUnitCutoff_secondDeriv_bounded

    Actual proof outline: Bound the continuous compactly supported second derivative: The two existing ASTIS properties discharge the compact-support and continuity premises. Choose a strictly positive witness: This positivity adjustment is explicit; it does not compute a sharp bound.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem smoothUnitCutoff_secondDeriv_bounded :
        ∃ C : ℝ, 0 < C ∧ ∀ x : ℝ, ‖deriv (deriv smoothUnitCutoff) x‖ ≤ C := by
      obtain ⟨C, hC⟩ :=
        smoothUnitCutoff_secondDeriv_hasCompactSupport.exists_bound_of_continuous
          smoothUnitCutoff_secondDeriv_continuous
      exact ⟨max C 1, lt_max_of_lt_right one_pos, fun x =>
        (hC x).trans (le_max_left C 1)⟩
    
    section Radial
    
    variable {E : Type*} [NormedAddCommGroup E]
    
    /-- The radial cutoff at scale `R`, given by `x ↦ smoothUnitCutoff (‖x‖ / R)`. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • Do not draw this as a prerequisite of the first-order radial C/R route, or invent a Hessian consumer from it.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • HasCompactSupport.exists_bound_of_continuous
    • lt_max_of_lt_right
    • le_max_left

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    The radial cutoff at a real scale

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff · def · Teaching coverage

    Statement

    For a normed additive commutative group E, a real scale R, and a point x∈E, define χ_R(x) by applying the fixed scalar cutoff q to the quotient ‖x‖/R. This definition is meaningful for every R; positivity is imposed only in later theorems.

    \[\chi_R:E\to\mathbb R,\qquad \chi_R(x):=q\!\left(\frac{\|x\|}{R}\right).\]

    All objects and hypotheses

    • E is a normed additive commutative group; no real scalar multiplication is required.
    • R is any real number and x∈E; there is no hypothesis R>0.

    Construction and meaning

    1. Form the radial argument and apply the scalar cutoff

    The norm produces a nonnegative real number. Divide this real number by R using Lean's total real division, then evaluate q. In particular, division by zero is defined to be zero, so the construction at R=0 is the constant-one function.

    \[\chi_R=q\circ(x\mapsto\|x\|/R),\qquad \chi_0(x)=q(0)=1.\]
    Corresponding Lean step

    smoothUnitCutoff (‖x‖ / R)

    Lean statement · radialSmoothCutoff

    `(R : ℝ) (x : E) : ℝ` states that the construction accepts a real scale and an E-valued point and returns a real value. Calling it smooth does not itself prove smoothness; the separate smoothness theorem has stronger ambient assumptions and requires a positive radius. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    noncomputable def radialSmoothCutoff (R : ℝ) (x : E) : ℝ

    Exact module and namespace context

    Lean construction · radialSmoothCutoff

    Construction, not a theorem proof. Form the radial argument and apply the scalar cutoff: This is the defining expression, not a proof. `noncomputable` allows use of the library's noncomputable smooth bump; `E` is inferred from the argument x.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    noncomputable def radialSmoothCutoff (R : ℝ) (x : E) : ℝ :=
      smoothUnitCutoff (‖x‖ / R)
    
    /-- The radial cutoff is one on the closed ball of radius `R`. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • The displayed R=0 observation explains totalization and is not a separately exported declaration.
    • Negative scales are allowed by the definition, but this module exports no negative-radius ball or derivative-bound theorem.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    No direct Mathlib call recorded; see the ASTIS parents.

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    The radial cutoff equals one on the inner closed ball

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_eq_one_of_norm_le · theorem · Teaching coverage

    Statement

    Let E be a normed additive commutative group, let R>0, and let x∈E satisfy ‖x‖≤R. Then the radius-R cutoff has value one at x.

    \[R>0,\ \|x\|\le R\quad\Longrightarrow\quad\chi_R(x)=1.\]

    All objects and hypotheses

    • E is a normed additive commutative group.
    • R∈ℝ, R>0, x∈E, and ‖x‖≤R.

    Mathematical proof

    1. Put the radial argument in the scalar plateau

    Because R is positive and norms are nonnegative, the quotient is nonnegative. The assumed norm bound gives a quotient at most one, hence absolute value at most one.

    \[0\le\frac{\|x\|}{R}\le1,\qquad \left|\frac{\|x\|}{R}\right|\le1.\]
    Corresponding Lean step

    rw [abs_of_nonneg (div_nonneg (norm_nonneg x) hR.le)]; exact div_le_one_of_le₀ hx hR.le

    2. Use the scalar plateau value

    The already-proved scalar cutoff equals one whenever its argument has absolute value at most one. Substitute the quotient.

    \[\chi_R(x)=q(\|x\|/R)=1.\]
    Corresponding Lean step

    apply smoothUnitCutoff_eq_one_of_abs_le_one

    Lean statement · radialSmoothCutoff_eq_one_of_norm_le

    Curly braces mark inferred R and x; explicit arguments `hR` and `hx` are proofs of positivity and membership in the inner closed ball. The conclusion is equality of real values, not yet a smoothness statement. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem radialSmoothCutoff_eq_one_of_norm_le {R : ℝ} (hR : 0 < R) {x : E}
        (hx : ‖x‖ ≤ R) : radialSmoothCutoff R x = 1

    Exact module and namespace context

    Lean proof · radialSmoothCutoff_eq_one_of_norm_le

    Actual proof outline: Put the radial argument in the scalar plateau: `hR` is the proof that R is positive, and `hx` is the norm bound. These lines discharge the absolute-value premise of the scalar plateau theorem. Use the scalar plateau value: `apply` reduces this radial value statement to the precise scalar condition verified in the preceding mathematical step.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem radialSmoothCutoff_eq_one_of_norm_le {R : ℝ} (hR : 0 < R) {x : E}
        (hx : ‖x‖ ≤ R) : radialSmoothCutoff R x = 1 := by
      apply smoothUnitCutoff_eq_one_of_abs_le_one
      rw [abs_of_nonneg (div_nonneg (norm_nonneg x) hR.le)]
      exact div_le_one_of_le₀ hx hR.le
    
    /-- The radial cutoff vanishes when `2 * R <= ||x||`. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • The sphere ‖x‖=R is included; this theorem alone does not say the function is locally constant at that sphere.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • div_le_one_of_le₀
    • abs_of_nonneg

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    The radial cutoff vanishes on and beyond the outer sphere

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_eq_zero_of_two_mul_le_norm · theorem · Teaching coverage

    Statement

    Let E be a normed additive commutative group and let R>0. For every x∈E with 2R≤‖x‖, the radius-R cutoff vanishes.

    \[R>0,\ 2R\le\|x\|\quad\Longrightarrow\quad\chi_R(x)=0.\]

    All objects and hypotheses

    • E is a normed additive commutative group.
    • R∈ℝ, R>0, x∈E, and 2R≤‖x‖.

    Mathematical proof

    1. Put the radial argument outside the scalar support region

    The radial argument is nonnegative. Dividing the assumed inequality by the positive radius shows it is at least two.

    \[\left|\frac{\|x\|}{R}\right|=\frac{\|x\|}{R}\ge\frac{2R}{R}=2.\]
    Corresponding Lean step

    rw [abs_of_nonneg (div_nonneg (norm_nonneg x) hR.le)]; calc (2 : ℝ) = 2 * R / R := by field_simp; _ ≤ ‖x‖ / R := div_le_div_of_nonneg_right hx hR.le

    2. Apply scalar vanishing

    The scalar cutoff vanishes whenever the argument has absolute value at least two. Applying that fact gives the desired zero value.

    \[\chi_R(x)=q(\|x\|/R)=0.\]
    Corresponding Lean step

    apply smoothUnitCutoff_eq_zero_of_two_le_abs

    Lean statement · radialSmoothCutoff_eq_zero_of_two_mul_le_norm

    The inequality is non-strict, so the exported zero region includes the boundary sphere of radius 2R. No differentiable structure on E is needed for this value assertion. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem radialSmoothCutoff_eq_zero_of_two_mul_le_norm {R : ℝ} (hR : 0 < R) {x : E}
        (hx : 2 * R ≤ ‖x‖) : radialSmoothCutoff R x = 0

    Exact module and namespace context

    Lean proof · radialSmoothCutoff_eq_zero_of_two_mul_le_norm

    Actual proof outline: Put the radial argument outside the scalar support region: The proof uses positivity both to remove absolute values and to preserve the inequality on division. `field_simp` verifies cancellation of the nonzero radius. Apply scalar vanishing: This is a radial substitution into the scalar zero-value theorem.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem radialSmoothCutoff_eq_zero_of_two_mul_le_norm {R : ℝ} (hR : 0 < R) {x : E}
        (hx : 2 * R ≤ ‖x‖) : radialSmoothCutoff R x = 0 := by
      apply smoothUnitCutoff_eq_zero_of_two_le_abs
      rw [abs_of_nonneg (div_nonneg (norm_nonneg x) hR.le)]
      calc
        (2 : ℝ) = 2 * R / R := by field_simp
        _ ≤ ‖x‖ / R := div_le_div_of_nonneg_right hx hR.le
    
    /-- Every radial cutoff value lies in `[0, 1]`, for any scale `R`. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • Zero at a boundary point is not a claim of local constancy there.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    • div_le_div_of_nonneg_right
    • abs_of_nonneg

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    Every radial cutoff value lies between zero and one

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_mem_Icc · theorem · Teaching coverage

    Statement

    For every normed additive commutative group E, every real scale R, and every x∈E, the cutoff value χ_R(x) belongs to [0,1].

    \[\forall R\in\mathbb R\ \forall x\in E,\qquad0\le\chi_R(x)\le1.\]

    All objects and hypotheses

    • E is a normed additive commutative group.
    • R is arbitrary, including zero or negative values; x is any point of E.

    Mathematical proof

    1. Reuse the scalar range theorem at the radial argument

    The scalar bound holds at every real input, so it applies directly to ‖x‖/R without a positivity condition on R.

    \[q(t)\in[0,1]\ \text{for all }t\in\mathbb R\quad\Longrightarrow\quad q(\|x\|/R)\in[0,1].\]
    Corresponding Lean step

    smoothUnitCutoff_mem_Icc _

    Lean statement · radialSmoothCutoff_mem_Icc

    `Set.Icc (0 : ℝ) 1` is the closed real interval; membership is a pair of inequalities. This is one of the statements intentionally valid at all totalized scales. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem radialSmoothCutoff_mem_Icc (R : ℝ) (x : E) :
        radialSmoothCutoff R x ∈ Set.Icc (0 : ℝ) 1

    Exact module and namespace context

    Lean proof · radialSmoothCutoff_mem_Icc

    Actual proof outline: Reuse the scalar range theorem at the radial argument: The underscore asks Lean to infer the particular input ‖x‖/R. This one-line wrapper does not supply new range analysis.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem radialSmoothCutoff_mem_Icc (R : ℝ) (x : E) :
        radialSmoothCutoff R x ∈ Set.Icc (0 : ℝ) 1 :=
      smoothUnitCutoff_mem_Icc _
    
    /-- Scaling the norm by a positive radius gives an operator-norm derivative
    bound of `1 / R`.  Mathlib's totalized `fderiv` makes the statement valid at
    the origin as well. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • The all-scale range bound does not extend positive-radius support or scale-decay theorems to nonpositive R.
    • No direct named invocation was found in the inspected cutoff example block Tests/Basic.lean:49–169; this is not a statement about all possible project consumers.

    Source and reuse

    ASTIS parents called

    Mathlib API called (external library)

    No direct Mathlib call recorded; see the ASTIS parents.

    Mathematical sources

    ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

    ASTIS mathematical exposition

    A totalized derivative bound for the scaled norm

    AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.fderiv_norm_div_bound · theorem · Teaching coverage

    Statement

    Let E be a real normed vector space and R>0. At every x∈E, the operator norm of Mathlib's totalized Fréchet derivative of y↦‖y‖/R is at most 1/R. No differentiability assumption on the norm at x is made.

    \[R>0\quad\Longrightarrow\quad\forall x\in E,\quad\left\|D_{\mathrm{tot}}\!\left(y\mapsto\frac{\|y\|}{R}\right)(x)\right\|_{\mathrm{op}}\le\frac1R.\]

    All objects and hypotheses

    • E is a normed additive commutative group equipped with a real normed-vector-space structure.
    • R∈ℝ with R>0; x∈E is arbitrary.
    • `D_tot` denotes `fderiv`: the Fréchet derivative when it exists and zero when it does not.

    Mathematical proof

    1. Prove a global Lipschitz estimate from the reverse triangle inequality

    The reverse triangle inequality bounds the change of the norm by the distance between points. Dividing by the positive radius gives Lipschitz constant 1/R.

    \[\left|\frac{\|y\|}{R}-\frac{\|z\|}{R}\right|=\frac{|\|y\|-\|z\||}{R}\le\frac{\|y-z\|}{R}=\frac1R\,d(y,z).\]
    Corresponding Lean step

    LipschitzWith.of_dist_le_mul fun y z => by ...; have hnorm : |‖y‖ - ‖z‖| ≤ ‖y - z‖ := abs_norm_sub_norm_le y z; rw [hdiv, abs_div, abs_of_pos hR]; ... div_le_div_of_nonneg_right hnorm hR.le

    2. Pass the Lipschitz constant to the totalized derivative

    Mathlib bounds the norm of fderiv by a global Lipschitz constant. Where a derivative exists, this is the ordinary derivative estimate. Where it does not exist, the totalized derivative is zero, which also satisfies the nonnegative bound.

    \[\|D_{\mathrm{tot}}(\|\cdot\|/R)(x)\|_{\mathrm{op}}\le\operatorname{Lip}(\|\cdot\|/R)\le1/R.\]
    Corresponding Lean step

    exact norm_fderiv_le_of_lipschitz ℝ hLip

    Lean statement · fderiv_norm_div_bound

    `[NormedSpace ℝ E]` is required to speak about a real Fréchet derivative. `fderiv ℝ ... x` is a continuous linear map E→ℝ, and the outer norm is its operator norm. The statement is not an assertion that the norm is smooth, or differentiable everywhere. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem fderiv_norm_div_bound [NormedSpace ℝ E] {R : ℝ} (hR : 0 < R) (x : E) :
        ‖fderiv ℝ (fun y : E => ‖y‖ / R) x‖ ≤ 1 / R

    Exact module and namespace context

    Lean proof · fderiv_norm_div_bound

    Actual proof outline: Prove a global Lipschitz estimate from the reverse triangle inequality: `hLip` packages exactly this inequality. The nonnegative real constant is stored as `⟨1 / R, by positivity⟩`; the omitted algebra is shown in the complete adjacent proof. Pass the Lipschitz constant to the totalized derivative: The library theorem is deliberately stated for totalized fderiv; it requires no `DifferentiableAt` premise. This matters in particular at the origin.

    Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

    theorem fderiv_norm_div_bound [NormedSpace ℝ E] {R : ℝ} (hR : 0 < R) (x : E) :
        ‖fderiv ℝ (fun y : E => ‖y‖ / R) x‖ ≤ 1 / R := by
      have hLip : LipschitzWith ⟨1 / R, by positivity⟩ (fun y : E => ‖y‖ / R) :=
        LipschitzWith.of_dist_le_mul fun y z => by
          have hnorm : |‖y‖ - ‖z‖| ≤ ‖y - z‖ := abs_norm_sub_norm_le y z
          simp only [Real.dist_eq]
          have hdiv : ‖y‖ / R - ‖z‖ / R = (‖y‖ - ‖z‖) / R := by ring
          rw [hdiv, abs_div, abs_of_pos hR]
          calc
            |‖y‖ - ‖z‖| / R ≤ ‖y - z‖ / R :=
              div_le_div_of_nonneg_right hnorm hR.le
            _ = 1 / R * ‖y - z‖ := by ring
            _ = 1 / R * dist y z := by rw [dist_eq_norm]
      exact norm_fderiv_le_of_lipschitz ℝ hLip
    
    /-- For positive scale, the radial cutoff is infinitely differentiable. -/

    Exact module and namespace context

    Scope and omitted-condition boundaries

    • No inner-product, finite-dimensionality, or nonzero-point hypothesis is present.
    • Do not read totalized fderiv as a proof of differentiability at the origin or at other nonsmooth points of a general norm.

    Source and reuse

    ASTIS parents called

      Mathlib API called (external library)

      • LipschitzWith.of_dist_le_mul
      • abs_norm_sub_norm_le
      • norm_fderiv_le_of_lipschitz
      • fderiv_zero_of_not_differentiableAt

      Mathematical sources

      ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

      ASTIS mathematical exposition

      Positive-radius radial cutoffs are smooth

      AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_contDiff · theorem · Teaching coverage

      Statement

      Let E be a real inner-product space, not necessarily finite-dimensional or complete, and let R>0. The function χ_R:E→ℝ is infinitely continuously differentiable on all of E, including the origin.

      \[R>0\quad\Longrightarrow\quad\chi_R\in C^\infty(E,\mathbb R).\]

      All objects and hypotheses

      • E is a normed additive commutative group with a real inner-product-space structure.
      • R∈ℝ and R>0. There is no finite-dimensionality, completeness, or nontriviality hypothesis.

      Mathematical proof

      1. Separate the inner plateau from the nonzero region

      Smoothness is checked at an arbitrary point x. Split according to whether ‖x‖<R. These two cases include the inner sphere in the second branch.

      \[E=\{x:\|x\|<R\}\cup\{x:R\le\|x\|\}.\]
      Corresponding Lean step

      rw [contDiff_iff_contDiffAt]; intro x; by_cases hx : ‖x‖ < R

      2. Use a constant neighborhood inside the plateau

      If ‖x‖<R, the ball around x of radius R−‖x‖ is a neighborhood. Every y in it satisfies ‖y‖≤‖x‖+‖y−x‖<R, so χ_R(y)=1. A function agreeing with a constant near x is smooth at x.

      \[d(y,x)<R-\|x\|\ \Longrightarrow\ \|y\|<R\ \Longrightarrow\ \chi_R(y)=1.\]
      Corresponding Lean step

      Metric.ball_mem_nhds x (sub_pos.mpr hx); radialSmoothCutoff_eq_one_of_norm_le hR; norm_add_le x (y - x); hconst.congr_of_eventuallyEq

      3. Compose smooth functions away from the origin

      In the remaining case R≤‖x‖, positivity implies x≠0. The norm on a real inner-product space is smooth at nonzero points. Compose it with division by the fixed R, then with the scalar smooth cutoff q.

      \[x\ne0,\qquad \chi_R=q\circ(t\mapsto t/R)\circ\|\cdot\|,\qquad \chi_R\text{ is }C^\infty\text{ at }x.\]
      Corresponding Lean step

      contDiffAt_norm ℝ hx_ne; contDiff_id.div_const R; hdiv.contDiffAt.comp x hnorm; smoothUnitCutoff_contDiff.contDiffAt.comp x hscaled

      Lean statement · radialSmoothCutoff_contDiff

      `ContDiff ℝ (⊤ : ℕ∞)` means derivatives of every finite order exist and are continuous. The inner-product assumption is used for smoothness of the norm away from zero; the plateau handles the origin. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_contDiff [InnerProductSpace ℝ E] {R : ℝ} (hR : 0 < R) :
          ContDiff ℝ (⊤ : ℕ∞) (radialSmoothCutoff R : E → ℝ)

      Exact module and namespace context

      Lean proof · radialSmoothCutoff_contDiff

      Actual proof outline: Separate the inner plateau from the nonzero region: `ContDiff` is global smoothness; `contDiff_iff_contDiffAt` allows a point-by-point argument. Use a constant neighborhood inside the plateau: `heq` records equality on a neighborhood, which is stronger than equality only at x. The library transfers smoothness of the constant function along this local equality. Compose smooth functions away from the origin: The norm-smoothness theorem is invoked only after proving x≠0. This avoids making a false smoothness assertion about the norm itself at zero.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_contDiff [InnerProductSpace ℝ E] {R : ℝ} (hR : 0 < R) :
          ContDiff ℝ (⊤ : ℕ∞) (radialSmoothCutoff R : E → ℝ) := by
        rw [contDiff_iff_contDiffAt]
        intro x
        by_cases hx : ‖x‖ < R
        · have heq : ∀ᶠ y in 𝓝 x, radialSmoothCutoff R y = 1 := by
            have hball : Metric.ball x (R - ‖x‖) ∈ 𝓝 x :=
              Metric.ball_mem_nhds x (sub_pos.mpr hx)
            filter_upwards [hball] with y hy
            apply radialSmoothCutoff_eq_one_of_norm_le hR
            rw [Metric.mem_ball, dist_eq_norm] at hy
            have hynorm : ‖y‖ ≤ ‖x‖ + ‖y - x‖ := by
              calc
                ‖y‖ = ‖x + (y - x)‖ := by congr 1; abel
                _ ≤ ‖x‖ + ‖y - x‖ := norm_add_le x (y - x)
            linarith
          have hconst : ContDiffAt ℝ (⊤ : ℕ∞) (fun _ : E => (1 : ℝ)) x :=
            contDiffAt_const
          apply hconst.congr_of_eventuallyEq
          filter_upwards [heq] with y hy
          simp [hy]
        · push Not at hx
          have hx_ne : x ≠ 0 := by
            intro hzero
            rw [hzero, norm_zero] at hx
            linarith
          have hnorm : ContDiffAt ℝ (⊤ : ℕ∞) (fun y : E => ‖y‖) x :=
            contDiffAt_norm ℝ hx_ne
          have hdiv : ContDiff ℝ (⊤ : ℕ∞) (fun t : ℝ => t / R) :=
            contDiff_id.div_const R
          have hscaled : ContDiffAt ℝ (⊤ : ℕ∞) (fun y : E => ‖y‖ / R) x :=
            hdiv.contDiffAt.comp x hnorm
          exact smoothUnitCutoff_contDiff.contDiffAt.comp x hscaled
      
      /-- A single positive constant controls the first derivative of every
      positive-scale radial cutoff by `C / R`.  The quantifier order records the
      scale-uniformity needed by cutoff exhaustion arguments. -/

      Exact module and namespace context

      Scope and omitted-condition boundaries

      • This proves genuine smoothness of χ_R, unlike the preceding totalized derivative bound for the scaled norm.
      • The source does not weaken the ambient assumption here to an arbitrary normed space.

      Source and reuse

      ASTIS parents called

      Mathlib API called (external library)

      • contDiff_iff_contDiffAt
      • Metric.ball_mem_nhds
      • norm_add_le
      • ContDiffAt.congr_of_eventuallyEq
      • contDiffAt_norm
      • ContDiff.div_const
      • ContDiffAt.comp

      Mathematical sources

      ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

      ASTIS mathematical exposition

      One constant controls every positive-radius first derivative

      AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_fderiv_bound · theorem · Teaching coverage

      Statement

      For any real inner-product space E, there is a positive real constant C such that for every positive radius R and every point x∈E, the operator norm of the Fréchet derivative of χ_R at x is at most C/R. The constant is chosen before R and x from a global bound on the fixed scalar cutoff derivative.

      \[\exists C>0\ \forall R>0\ \forall x\in E,\qquad\|D\chi_R(x)\|_{\mathrm{op}}\le\frac CR.\]

      All objects and hypotheses

      • E is a normed additive commutative group with a real inner-product-space structure; no finite-dimensionality or completeness assumption.
      • C is existential and strictly positive; R>0 and x∈E are universally quantified after C.
      • The derivative is genuine here because χ_R is smooth for R>0; the Lean expression remains the totalized `fderiv`.

      Mathematical proof

      1. Fix the scalar derivative constant before choosing the radius

      The scalar boundedness theorem supplies C>0 such that |q′(t)|≤C for every t. Keep this same C for all radii and points.

      \[\exists C>0\ \forall t\in\mathbb R,\ |q'(t)|\le C.\]
      Corresponding Lean step

      obtain ⟨C, hC_pos, hC_bound⟩ := smoothUnitCutoff_deriv_bounded; refine ⟨C, hC_pos, ?_⟩; intro R hR x

      2. The derivative vanishes strictly inside the plateau

      If ‖x‖<R, the triangle inequality shows that χ_R agrees with one on a neighborhood of x of radius R−‖x‖. Its derivative is therefore the zero linear map, and the desired nonnegative upper bound follows.

      \[\|x\|<R\ \Longrightarrow\ D\chi_R(x)=D(1)(x)=0,\qquad0\le C/R.\]
      Corresponding Lean step

      Metric.eventually_nhds_iff.mpr ⟨R - ‖x‖, hradius, ?_⟩; radialSmoothCutoff_eq_one_of_norm_le hR; (Filter.EventuallyEq.fderiv_eq h_eq).trans hconst; div_nonneg hC_pos.le hR.le

      3. Use genuine differentiability for the chain rule outside the inner ball

      If R≤‖x‖, then x≠0. The inner-product norm is differentiable there, so h(y)=‖y‖/R is differentiable at x; q is differentiable everywhere. The Fréchet chain rule therefore applies.

      \[D\chi_R(x)=Dq(\|x\|/R)\circ Dh(x),\qquad h(y)=\|y\|/R.\]
      Corresponding Lean step

      (contDiffAt_norm ℝ hx_ne).differentiableAt WithTop.top_ne_zero; hnorm.mul_const R⁻¹; smoothUnitCutoff_contDiff.differentiable ...; fderiv_comp x hcutoff_diff hnorm_diff

      4. Multiply the two operator-norm bounds

      The norm of a composition is at most the product of the norms. The scalar derivative factor is at most C; the scaled-norm derivative factor is at most 1/R by the preceding Lipschitz-based lemma. Their product is C/R.

      \[\|D\chi_R(x)\|_{\mathrm{op}}\le |q'(\|x\|/R)|\,\|Dh(x)\|_{\mathrm{op}}\le C(1/R)=C/R.\]
      Corresponding Lean step

      ContinuousLinearMap.opNorm_comp_le _ _; rw [← norm_deriv_eq_norm_fderiv]; exact hC_bound _; exact fderiv_norm_div_bound hR x; ... _ = C / R := by ring

      Lean statement · radialSmoothCutoff_fderiv_bound

      The existential statement does not give a numerical or optimal value of C. The proof uses only first-order ingredients and the scalar first-derivative bound; it neither invokes nor requires the later second-order API. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_fderiv_bound [InnerProductSpace ℝ E] :
          ∃ C : ℝ, 0 < C ∧ ∀ R : ℝ, 0 < R → ∀ x : E,
            ‖fderiv ℝ (radialSmoothCutoff R : E → ℝ) x‖ ≤ C / R

      Exact module and namespace context

      Lean proof · radialSmoothCutoff_fderiv_bound

      Actual proof outline: Fix the scalar derivative constant before choosing the radius: Introducing R and x only after selecting C records the required scale-uniform quantifier order. The derivative vanishes strictly inside the plateau: The proof constructs actual neighborhood equality `h_eq`; it does not infer derivative equality from a single point value. Use genuine differentiability for the chain rule outside the inner ball: `fderiv_comp` is called with explicit differentiability witnesses for both factors. Totalization alone would not justify an unrestricted chain rule. Multiply the two operator-norm bounds: `norm_deriv_eq_norm_fderiv` identifies the norm of the scalar derivative with the operator norm of its one-dimensional Fréchet derivative.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_fderiv_bound [InnerProductSpace ℝ E] :
          ∃ C : ℝ, 0 < C ∧ ∀ R : ℝ, 0 < R → ∀ x : E,
            ‖fderiv ℝ (radialSmoothCutoff R : E → ℝ) x‖ ≤ C / R := by
        obtain ⟨C, hC_pos, hC_bound⟩ := smoothUnitCutoff_deriv_bounded
        refine ⟨C, hC_pos, ?_⟩
        intro R hR x
        by_cases hxR : ‖x‖ < R
        · have h_eq : ∀ᶠ y in 𝓝 x, radialSmoothCutoff R y = 1 := by
            have hradius : 0 < R - ‖x‖ := sub_pos.mpr hxR
            refine Metric.eventually_nhds_iff.mpr ⟨R - ‖x‖, hradius, ?_⟩
            intro y hy
            apply radialSmoothCutoff_eq_one_of_norm_le hR
            rw [dist_eq_norm] at hy
            have hynorm : ‖y‖ ≤ ‖x‖ + ‖y - x‖ := by
              calc
                ‖y‖ = ‖x + (y - x)‖ := by congr 1; abel
                _ ≤ ‖x‖ + ‖y - x‖ := norm_add_le x (y - x)
            linarith
          have hfderiv_eq : fderiv ℝ (radialSmoothCutoff R : E → ℝ) x = 0 := by
            have hconst : fderiv ℝ (fun _ : E => (1 : ℝ)) x = 0 := by simp
            exact (Filter.EventuallyEq.fderiv_eq h_eq).trans hconst
          rw [hfderiv_eq, norm_zero]
          exact div_nonneg hC_pos.le hR.le
        · push Not at hxR
          have hx_ne : x ≠ 0 := by
            intro hzero
            rw [hzero, norm_zero] at hxR
            linarith
          have hnorm_diff : DifferentiableAt ℝ (fun y : E => ‖y‖ / R) x := by
            have hnorm : DifferentiableAt ℝ (fun y : E => ‖y‖) x :=
              (contDiffAt_norm ℝ hx_ne).differentiableAt WithTop.top_ne_zero
            simpa only [div_eq_mul_inv] using hnorm.mul_const R⁻¹
          have hcutoff_diff : DifferentiableAt ℝ smoothUnitCutoff (‖x‖ / R) :=
            smoothUnitCutoff_contDiff.differentiable
              (WithTop.coe_ne_zero.mpr WithTop.top_ne_zero) (‖x‖ / R)
          have hchain :
              fderiv ℝ (radialSmoothCutoff R : E → ℝ) x =
                fderiv ℝ smoothUnitCutoff (‖x‖ / R) ∘L
                  fderiv ℝ (fun y : E => ‖y‖ / R) x := by
            unfold radialSmoothCutoff
            exact fderiv_comp x hcutoff_diff hnorm_diff
          rw [hchain]
          calc
            ‖fderiv ℝ smoothUnitCutoff (‖x‖ / R) ∘L
                fderiv ℝ (fun y : E => ‖y‖ / R) x‖
                ≤ ‖fderiv ℝ smoothUnitCutoff (‖x‖ / R)‖ *
                    ‖fderiv ℝ (fun y : E => ‖y‖ / R) x‖ :=
                  ContinuousLinearMap.opNorm_comp_le _ _
            _ ≤ C * (1 / R) := by
              apply mul_le_mul
              · rw [← norm_deriv_eq_norm_fderiv]
                exact hC_bound _
              · exact fderiv_norm_div_bound hR x
              · exact norm_nonneg _
              · exact hC_pos.le
            _ = C / R := by ring
      
      /-- The totalized derivative of the radial cutoff vanishes throughout the
      outer zero region, including its boundary sphere.  At the boundary the cutoff
      is a global minimum rather than locally constant; `IsLocalMin.fderiv_eq_zero`
      records that distinction. -/

      Exact module and namespace context

      Scope and omitted-condition boundaries

      • This theorem is not a Hessian, Laplacian, or second-derivative estimate.
      • A fixed E is an ambient parameter; although the construction reuses the fixed scalar bound, the declaration does not quantify one C simultaneously over all space types.

      Source and reuse

      ASTIS parents called

      Mathlib API called (external library)

      • Metric.eventually_nhds_iff
      • Filter.EventuallyEq.fderiv_eq
      • contDiffAt_norm
      • DifferentiableAt.mul_const
      • fderiv_comp
      • ContinuousLinearMap.opNorm_comp_le
      • norm_deriv_eq_norm_fderiv

      Mathematical sources

      ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

      ASTIS mathematical exposition

      The totalized derivative vanishes throughout the outer zero region

      AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_fderiv_eq_zero_of_two_mul_le_norm · theorem · Teaching coverage

      Statement

      Let E be a real normed vector space, let R>0, and let x∈E satisfy 2R≤‖x‖. Then Mathlib's totalized Fréchet derivative of χ_R at x is the zero continuous linear map. This includes the outer boundary sphere and does not assume an inner-product norm or differentiability of χ_R.

      \[R>0,\ 2R\le\|x\|\quad\Longrightarrow\quad D_{\mathrm{tot}}\chi_R(x)=0.\]

      All objects and hypotheses

      • E is a normed additive commutative group equipped with a real normed-vector-space structure.
      • R>0, x∈E, and 2R≤‖x‖.
      • No differentiability or inner-product-space hypothesis is assumed; `fderiv` is totalized.

      Mathematical proof

      1. Recognize a global, hence local, minimum

      The outer value theorem gives χ_R(x)=0. The range theorem gives χ_R(y)≥0 for every y∈E. Thus x is a global minimum, and in particular a local minimum.

      \[\chi_R(x)=0\le\chi_R(y)\quad(\forall y\in E).\]
      Corresponding Lean step

      rw [radialSmoothCutoff_eq_zero_of_two_mul_le_norm hR hx]; exact Filter.Eventually.of_forall fun y => (radialSmoothCutoff_mem_Icc R y).1

      2. Apply the totalized form of Fermat's rule

      At a differentiable local minimum the derivative is zero. Mathlib's theorem states this for totalized fderiv: if differentiability fails, fderiv is zero by definition instead. Consequently the same conclusion holds without an explicit differentiability assumption.

      \[x\text{ a local minimum of }\chi_R\quad\Longrightarrow\quad D_{\mathrm{tot}}\chi_R(x)=0.\]
      Corresponding Lean step

      apply IsLocalMin.fderiv_eq_zero

      Lean statement · radialSmoothCutoff_fderiv_eq_zero_of_two_mul_le_norm

      The right-hand zero is the zero continuous linear map, not just a zero real value. At ‖x‖=2R the proof uses a minimum, not an unjustified locally-constant neighborhood. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_fderiv_eq_zero_of_two_mul_le_norm [NormedSpace ℝ E]
          {R : ℝ} (hR : 0 < R) {x : E} (hx : 2 * R ≤ ‖x‖) :
          fderiv ℝ (radialSmoothCutoff R : E → ℝ) x = 0

      Exact module and namespace context

      Lean proof · radialSmoothCutoff_fderiv_eq_zero_of_two_mul_le_norm

      Actual proof outline: Recognize a global, hence local, minimum: `Filter.Eventually.of_forall` turns the inequality at all points into the neighborhood inequality defining a local minimum. Apply the totalized form of Fermat's rule: This exact library theorem handles both differentiability cases. The ASTIS proof supplies the local-minimum premise.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_fderiv_eq_zero_of_two_mul_le_norm [NormedSpace ℝ E]
          {R : ℝ} (hR : 0 < R) {x : E} (hx : 2 * R ≤ ‖x‖) :
          fderiv ℝ (radialSmoothCutoff R : E → ℝ) x = 0 := by
        apply IsLocalMin.fderiv_eq_zero
        change ∀ᶠ y in 𝓝 x, radialSmoothCutoff R x ≤ radialSmoothCutoff R y
        rw [radialSmoothCutoff_eq_zero_of_two_mul_le_norm hR hx]
        exact Filter.Eventually.of_forall fun y => (radialSmoothCutoff_mem_Icc R y).1
      
      /-- The support of the radial cutoff lies in the closed ball of radius `2 * R`. -/

      Exact module and namespace context

      Scope and omitted-condition boundaries

      • On a general normed space this statement does not itself establish differentiability.
      • No strict inequality or omission of the boundary sphere should be substituted for the existing statement.

      Source and reuse

      ASTIS parents called

      Mathlib API called (external library)

      • IsLocalMin.fderiv_eq_zero
      • Filter.Eventually.of_forall
      • fderiv_zero_of_not_differentiableAt

      Mathematical sources

      ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

      ASTIS mathematical exposition

      The ordinary support lies in the outer closed ball

      AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_support_subset_closedBall · theorem · Teaching coverage

      Statement

      Let E be a normed additive commutative group and R>0. Every point where χ_R is nonzero lies in the closed ball about zero of radius 2R.

      \[\operatorname{supp}\chi_R:=\{x:\chi_R(x)\ne0\}\subseteq\overline B(0,2R)=\{x:\|x\|\le2R\}.\]

      All objects and hypotheses

      • E is a normed additive commutative group.
      • R∈ℝ with R>0.
      • Here `Function.support` means the nonzero set, not its closure.

      Mathematical proof

      1. Exclude a nonzero value beyond the outer ball

      Take x in the ordinary support. If x were outside the closed ball, then 2R<‖x‖. The outer zero-value theorem would force χ_R(x)=0, contradicting membership in the support.

      \[\chi_R(x)\ne0,\ \|x\|>2R\quad\Longrightarrow\quad\chi_R(x)=0\quad\text{(contradiction)}.\]
      Corresponding Lean step

      rw [Metric.mem_closedBall, dist_zero_right]; by_contra hout; push Not at hout; have hzero := radialSmoothCutoff_eq_zero_of_two_mul_le_norm hR hout.le; exact hx hzero

      Lean statement · radialSmoothCutoff_support_subset_closedBall

      The theorem is an inclusion, not an exact support description. No compactness or smoothness is needed to prove it. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_support_subset_closedBall {R : ℝ} (hR : 0 < R) :
          Function.support (radialSmoothCutoff R : E → ℝ) ⊆
            Metric.closedBall 0 (2 * R)

      Exact module and namespace context

      Lean proof · radialSmoothCutoff_support_subset_closedBall

      Actual proof outline: Exclude a nonzero value beyond the outer ball: Support membership `hx` is definitionally the assertion that the function value is nonzero. The contradiction proves the closed-ball inequality.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_support_subset_closedBall {R : ℝ} (hR : 0 < R) :
          Function.support (radialSmoothCutoff R : E → ℝ) ⊆
            Metric.closedBall 0 (2 * R) := by
        intro x hx
        rw [Metric.mem_closedBall, dist_zero_right]
        by_contra hout
        push Not at hout
        have hzero : radialSmoothCutoff R x = 0 :=
          radialSmoothCutoff_eq_zero_of_two_mul_le_norm hR hout.le
        exact hx hzero
      
      /-- The topological support of the radial cutoff lies in the same closed ball. -/

      Exact module and namespace context

      Scope and omitted-condition boundaries

      • Ordinary support is not assumed closed, and this statement alone does not assert compact support in an arbitrary normed group.
      • No direct named invocation was found in the inspected cutoff example block Tests/Basic.lean:49–169; this is not a statement about all possible project consumers.

      Source and reuse

      ASTIS parents called

      Mathlib API called (external library)

      • Metric.mem_closedBall
      • dist_zero_right

      Mathematical sources

      ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

      ASTIS mathematical exposition

      The closed support lies in the same outer closed ball

      AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_tsupport_subset_closedBall · theorem · Teaching coverage

      Statement

      Let E be a normed additive commutative group and R>0. The topological support of χ_R, defined as the closure of its nonzero set, is contained in the closed ball about zero of radius 2R.

      \[\operatorname{tsupp}\chi_R:=\overline{\{x:\chi_R(x)\ne0\}}\subseteq\overline B(0,2R).\]

      All objects and hypotheses

      • E is a normed additive commutative group.
      • R∈ℝ with R>0.
      • Topological support is the closure of ordinary support.

      Mathematical proof

      1. Take closure inside the closed containing ball

      The preceding theorem places the ordinary support inside the outer closed ball. Because that ball is closed, it also contains the closure of the support.

      \[\operatorname{supp}\chi_R\subseteq\overline B(0,2R),\quad\overline B(0,2R)\text{ closed}\quad\Longrightarrow\quad\overline{\operatorname{supp}\chi_R}\subseteq\overline B(0,2R).\]
      Corresponding Lean step

      closure_minimal (radialSmoothCutoff_support_subset_closedBall hR) Metric.isClosed_closedBall

      Lean statement · radialSmoothCutoff_tsupport_subset_closedBall

      This is a short topological wrapper around the ordinary-support inclusion. It is useful for compact-support arguments, which concern the closed support. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_tsupport_subset_closedBall {R : ℝ} (hR : 0 < R) :
          tsupport (radialSmoothCutoff R : E → ℝ) ⊆
            Metric.closedBall 0 (2 * R)

      Exact module and namespace context

      Lean proof · radialSmoothCutoff_tsupport_subset_closedBall

      Actual proof outline: Take closure inside the closed containing ball: `closure_minimal` is the topological fact that the closure is contained in every closed superset. The apparent change from support to tsupport is exactly this closure operation.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_tsupport_subset_closedBall {R : ℝ} (hR : 0 < R) :
          tsupport (radialSmoothCutoff R : E → ℝ) ⊆
            Metric.closedBall 0 (2 * R) := by
        exact closure_minimal
          (radialSmoothCutoff_support_subset_closedBall hR) Metric.isClosed_closedBall
      
      /-- In finite dimension, a positive-scale radial cutoff has compact support. -/

      Exact module and namespace context

      Scope and omitted-condition boundaries

      • Containment in a bounded closed ball is not yet compactness in infinite dimension.

      Source and reuse

      ASTIS parents called

      Mathlib API called (external library)

      • closure_minimal
      • Metric.isClosed_closedBall

      Mathematical sources

      ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

      ASTIS mathematical exposition

      Finite dimension makes the radial cutoff compactly supported

      AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_hasCompactSupport · theorem · Teaching coverage

      Statement

      Let E be a finite-dimensional real normed vector space and R>0. Then χ_R has compact support: the closure of its nonzero set is compact.

      \[\dim_{\mathbb R}E<\infty,\ R>0\quad\Longrightarrow\quad\operatorname{tsupp}\chi_R\text{ is compact}.\]

      All objects and hypotheses

      • E is a normed additive commutative group with a real normed-vector-space structure and `FiniteDimensional ℝ E`.
      • R∈ℝ with R>0; no inner-product-space or nontriviality assumption is present.

      Mathematical proof

      1. Place the closed support inside a compact ball

      The closed support is closed by its definition as a closure and lies in the closed ball of radius 2R. In a finite-dimensional real normed vector space, this ball is compact.

      \[\operatorname{tsupp}\chi_R\subseteq\overline B(0,2R),\qquad\overline B(0,2R)\text{ compact}.\]
      Corresponding Lean step

      isCompact_closedBall (0 : E) (2 * R); isClosed_closure; radialSmoothCutoff_tsupport_subset_closedBall hR

      2. Use compactness of a closed subset

      A closed subset of a compact set is compact. This is precisely the definition of compact support for χ_R.

      \[\operatorname{tsupp}\chi_R\text{ closed in a compact containing ball}\quad\Longrightarrow\quad\operatorname{HasCompactSupport}(\chi_R).\]
      Corresponding Lean step

      rw [hasCompactSupport_def]; exact IsCompact.of_isClosed_subset ...

      Lean statement · radialSmoothCutoff_hasCompactSupport

      The finite-dimensional assumption enters the compactness argument rather than the value or support-inclusion lemmas. This theorem applies to arbitrary real norms, not just inner-product norms. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_hasCompactSupport [NormedSpace ℝ E] [FiniteDimensional ℝ E]
          {R : ℝ} (hR : 0 < R) :
          HasCompactSupport (radialSmoothCutoff R : E → ℝ)

      Exact module and namespace context

      Lean proof · radialSmoothCutoff_hasCompactSupport

      Actual proof outline: Place the closed support inside a compact ball: Finite-dimensionality supplies the proper-space instance needed for the compact closed-ball fact; smoothness of χ_R is not used. Use compactness of a closed subset: `hasCompactSupport_def` unfolds the predicate into compactness of tsupport; `IsCompact.of_isClosed_subset` finishes that exact goal.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_hasCompactSupport [NormedSpace ℝ E] [FiniteDimensional ℝ E]
          {R : ℝ} (hR : 0 < R) :
          HasCompactSupport (radialSmoothCutoff R : E → ℝ) := by
        rw [hasCompactSupport_def]
        exact IsCompact.of_isClosed_subset
          (isCompact_closedBall (0 : E) (2 * R)) isClosed_closure
          (radialSmoothCutoff_tsupport_subset_closedBall hR)
      
      /-- A single positive constant controls the second iterated Fréchet
      derivative of every positive-scale radial cutoff by `C / R^2`.
      
      The proof first bounds the second derivative of the unit-scale radial cutoff
      using continuity and compact support.  It then writes the radius-`R` cutoff as
      the unit cutoff composed with scalar dilation and applies Mathlib's exact
      iterated-derivative composition rule for continuous linear maps. -/

      Exact module and namespace context

      Scope and omitted-condition boundaries

      • No compact-support theorem for an arbitrary infinite-dimensional space is asserted.

      Source and reuse

      ASTIS parents called

      Mathlib API called (external library)

      • hasCompactSupport_def
      • isCompact_closedBall
      • isClosed_closure
      • IsCompact.of_isClosed_subset

      Mathematical sources

      ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

      ASTIS mathematical exposition

      An existing uniform second-derivative bound under dilation

      AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_iteratedFDeriv_two_bound · theorem · Teaching coverage

      Statement

      Let E be a nontrivial finite-dimensional real inner-product space. There is a positive real constant C such that for every positive R and every x∈E, the operator norm of the second iterated Fréchet derivative of χ_R at x is at most C/R². The constant is chosen from the fixed unit-radius radial function before R and x.

      \[\exists C>0\ \forall R>0\ \forall x\in E,\qquad\|D^2\chi_R(x)\|_{\mathrm{op}}\le\frac{C}{R^2}.\]

      All objects and hypotheses

      • E is a normed additive commutative group with a real inner-product-space structure.
      • E is finite-dimensional over ℝ and nontrivial, exactly as required by the existing declaration.
      • The second iterated Fréchet derivative is a continuous bilinear map; its norm is the multilinear operator norm.
      • C>0 is existential; R>0 and x∈E are quantified after it.

      Mathematical proof

      1. Obtain continuity and compact support of the unit-radius second derivative

      The already-proved smoothness of χ_1 implies continuity of its second iterated Fréchet derivative. The compact-support theorem for χ_1 and Mathlib's support theorem for iterated derivatives give compact support of this derivative.

      \[\chi_1\in C^\infty,\quad \operatorname{HasCompactSupport}(\chi_1)\quad\Longrightarrow\quad D^2\chi_1\text{ continuous and compactly supported}.\]
      Corresponding Lean step

      radialSmoothCutoff_contDiff one_pos; hunit_smooth.continuous_iteratedFDeriv ...; (radialSmoothCutoff_hasCompactSupport one_pos).iteratedFDeriv 2

      2. Choose one positive global bound at unit radius

      A continuous compactly supported map has globally bounded norm. Take a bound B for D²χ_1 and enlarge it to C=max(B,1)>0.

      \[\|D^2\chi_1(z)\|_{\mathrm{op}}\le B\le C:=\max(B,1)\qquad(\forall z\in E).\]
      Corresponding Lean step

      hunit_support.exists_bound_of_continuous hunit_continuous; let C' : ℝ := max C 1; have hC'_pos : 0 < C' := lt_max_of_lt_right one_pos

      3. Represent the radius change by a bounded linear dilation

      For R>0 let L_R(y)=R⁻¹y. This is a continuous linear map with operator norm at most 1/R, because ‖L_R y‖=(1/R)‖y‖.

      \[L_R=R^{-1}I_E,\qquad\|L_Ry\|=\frac{\|y\|}{R},\qquad\|L_R\|_{\mathrm{op}}\le\frac1R.\]
      Corresponding Lean step

      let L : E →L[ℝ] E := R⁻¹ • ContinuousLinearMap.id ℝ E; refine L.opNorm_le_bound (by positivity) ?_; rw [norm_smul, Real.norm_eq_abs, abs_inv, abs_of_pos hR]

      4. Express every cutoff as the unit cutoff after dilation

      The norm identity for positive dilation gives χ_R(y)=q(‖y‖/R)=χ_1(L_R y) at every y.

      \[\chi_R=\chi_1\circ L_R.\]
      Corresponding Lean step

      have hfun : (radialSmoothCutoff R : E → ℝ) = (radialSmoothCutoff 1 : E → ℝ) ∘ L := by funext y; simp only [radialSmoothCutoff, Function.comp_apply, L, smul_apply, ContinuousLinearMap.id_apply, div_one]; rw [norm_smul, Real.norm_eq_abs, abs_inv, abs_of_pos hR]; ring_nf

      5. Apply the exact second-derivative rule for a linear inner map

      Because the inner map is linear, the second derivative of the composite is the unit-radius second derivative with L_R applied in each argument. There are no derivatives of order two of L_R and no extra chain-rule terms.

      \[D^2\chi_R(x)[u,v]=D^2\chi_1(L_Rx)[L_Ru,L_Rv].\]
      Corresponding Lean step

      rw [hfun]; rw [L.iteratedFDeriv_comp_right hunit_smooth x (i := 2) ...]

      6. Bound both linear slots and simplify the scale

      The multilinear composition norm is bounded by the outer norm times the product of the two inner operator norms. Combine the unit bound and dilation bound to obtain C/R².

      \[\|D^2\chi_R(x)\|_{\mathrm{op}}\le\|D^2\chi_1(L_Rx)\|_{\mathrm{op}}\|L_R\|_{\mathrm{op}}^2\le C(1/R)^2=C/R^2.\]
      Corresponding Lean step

      ContinuousMultilinearMap.norm_compContinuousLinearMap_le _ _; ... ∏ _ : Fin 2, ‖L‖ ...; simp; gcongr; exact (hC (L x)).trans (le_max_left C 1); ... := by ring

      Lean statement · radialSmoothCutoff_iteratedFDeriv_two_bound

      `iteratedFDeriv ℝ 2` is the existing order-two continuous multilinear derivative API. This theorem already occurs in the current checkout; documenting it neither adds a Hessian/Laplacian declaration nor makes it a prerequisite of the separate first-order proof route. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_iteratedFDeriv_two_bound
          [InnerProductSpace ℝ E] [FiniteDimensional ℝ E] [Nontrivial E] :
          ∃ C : ℝ, 0 < C ∧ ∀ R : ℝ, 0 < R → ∀ x : E,
            ‖iteratedFDeriv ℝ 2 (radialSmoothCutoff R : E → ℝ) x‖ ≤ C / R ^ 2

      Exact module and namespace context

      Lean proof · radialSmoothCutoff_iteratedFDeriv_two_bound

      Actual proof outline: Obtain continuity and compact support of the unit-radius second derivative: The code obtains its second-order input from the unit radial function itself. It does not call `smoothUnitCutoff_secondDeriv_bounded` or the first-order radial bound. Choose one positive global bound at unit radius: The names `C` and `C'` in Lean correspond to B and the positive C in the exposition. This choice precedes `intro R hR x`. Represent the radius change by a bounded linear dilation: `E →L[ℝ] E` is the type of real continuous linear maps. The positivity assumption removes the absolute value of R in the scaling norm. Express every cutoff as the unit cutoff after dilation: `funext` proves equality of functions by evaluating at an arbitrary point. This is an exact scaling identity, not an asymptotic approximation. Apply the exact second-derivative rule for a linear inner map: The actual helper is `ContinuousLinearMap.iteratedFDeriv_comp_right`; `compContinuousLinearMap (fun _ => L)` places the same linear map in both multilinear slots. Bound both linear slots and simplify the scale: The product indexed by `Fin 2` has exactly two factors. The final algebra is the displayed R⁻² scale.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_iteratedFDeriv_two_bound
          [InnerProductSpace ℝ E] [FiniteDimensional ℝ E] [Nontrivial E] :
          ∃ C : ℝ, 0 < C ∧ ∀ R : ℝ, 0 < R → ∀ x : E,
            ‖iteratedFDeriv ℝ 2 (radialSmoothCutoff R : E → ℝ) x‖ ≤ C / R ^ 2 := by
        have hunit_smooth : ContDiff ℝ (⊤ : ℕ∞)
            (radialSmoothCutoff 1 : E → ℝ) :=
          radialSmoothCutoff_contDiff one_pos
        have hunit_continuous : Continuous
            (iteratedFDeriv ℝ 2 (radialSmoothCutoff 1 : E → ℝ)) :=
          hunit_smooth.continuous_iteratedFDeriv
            (WithTop.coe_le_coe.mpr (le_top : (2 : ℕ∞) ≤ ⊤))
        have hunit_support : HasCompactSupport
            (iteratedFDeriv ℝ 2 (radialSmoothCutoff 1 : E → ℝ)) :=
          (radialSmoothCutoff_hasCompactSupport one_pos).iteratedFDeriv 2
        obtain ⟨C, hC⟩ :=
          hunit_support.exists_bound_of_continuous hunit_continuous
        let C' : ℝ := max C 1
        have hC'_pos : 0 < C' := lt_max_of_lt_right one_pos
        refine ⟨C', hC'_pos, ?_⟩
        intro R hR x
        let L : E →L[ℝ] E := R⁻¹ • ContinuousLinearMap.id ℝ E
        have hL_norm : ‖L‖ ≤ 1 / R := by
          refine L.opNorm_le_bound (by positivity) ?_
          intro y
          simp only [L, smul_apply, ContinuousLinearMap.id_apply]
          rw [norm_smul, Real.norm_eq_abs, abs_inv, abs_of_pos hR]
          simp [one_div]
        have hfun : (radialSmoothCutoff R : E → ℝ) =
            (radialSmoothCutoff 1 : E → ℝ) ∘ L := by
          funext y
          simp only [radialSmoothCutoff, Function.comp_apply, L,
            smul_apply, ContinuousLinearMap.id_apply, div_one]
          rw [norm_smul, Real.norm_eq_abs, abs_inv, abs_of_pos hR]
          ring_nf
        rw [hfun]
        rw [L.iteratedFDeriv_comp_right hunit_smooth x
          (i := 2) (WithTop.coe_le_coe.mpr (le_top : (2 : ℕ∞) ≤ ⊤))]
        calc
          ‖(iteratedFDeriv ℝ 2 (radialSmoothCutoff 1 : E → ℝ) (L x)).compContinuousLinearMap
              (fun _ => L)‖ ≤
              ‖iteratedFDeriv ℝ 2 (radialSmoothCutoff 1 : E → ℝ) (L x)‖ *
                ∏ _ : Fin 2, ‖L‖ :=
            ContinuousMultilinearMap.norm_compContinuousLinearMap_le _ _
          _ = ‖iteratedFDeriv ℝ 2 (radialSmoothCutoff 1 : E → ℝ) (L x)‖ * ‖L‖ ^ 2 := by
            simp
          _ ≤ C' * (1 / R) ^ 2 := by
            gcongr
            exact (hC (L x)).trans (le_max_left C 1)
          _ = C' / R ^ 2 := by ring
      
      /-- At each fixed point, the positive-scale radial cutoffs tend to one as the scale diverges. -/

      Exact module and namespace context

      Scope and omitted-condition boundaries

      • The module header's statement that no second-derivative bound is supplied is stale relative to this existing declaration.
      • Retain the finite-dimensional and nontrivial hypotheses even if some could potentially be weakened mathematically; no such generalization is proved here.
      • No new Hessian, Laplacian, trace estimate, or inferred downstream consumer is asserted.
      • No numerical value or optimality of C is given.
      • The opening module comment is stale: this second-order declaration already exists in the current checkout. Documenting it does not introduce a new Hessian branch or make it a prerequisite of a first-order cutoff argument.

      Source and reuse

      ASTIS parents called

      Mathlib API called (external library)

      • ContDiff.continuous_iteratedFDeriv
      • HasCompactSupport.iteratedFDeriv
      • HasCompactSupport.exists_bound_of_continuous
      • ContinuousLinearMap.opNorm_le_bound
      • norm_smul
      • ContinuousLinearMap.iteratedFDeriv_comp_right
      • ContinuousMultilinearMap.norm_compContinuousLinearMap_le

      Mathematical sources

      ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

      ASTIS mathematical exposition

      At each fixed point the cutoffs are eventually equal to one

      AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.radialSmoothCutoff_tendsto_one · theorem · Teaching coverage

      Statement

      For any normed additive commutative group E and any fixed x∈E, χ_R(x) tends to one as the real scale R tends to positive infinity. Indeed it equals one for every R≥‖x‖+1.

      \[\forall x\in E,\qquad \lim_{R\to+\infty}\chi_R(x)=1,\qquad R\ge\|x\|+1\Longrightarrow\chi_R(x)=1.\]

      All objects and hypotheses

      • E is a normed additive commutative group and x∈E is fixed.
      • The limit is over all real R along `atTop`; sufficiently large R are automatically positive.

      Mathematical proof

      1. Choose a threshold depending on the fixed point

      Take R₀=‖x‖+1. If R≥R₀, then R>0 and ‖x‖≤R, so the point lies on the inner plateau of χ_R.

      \[R\ge R_0:=\|x\|+1\quad\Longrightarrow\quad R>0,\quad\|x\|\le R.\]
      Corresponding Lean step

      apply tendsto_atTop_of_eventually_const (i₀ := ‖x‖ + 1); intro R hR; have hR_pos : 0 < R := by ...

      2. Use eventual equality to obtain the limit

      The inner-ball value theorem gives χ_R(x)=1 beyond this threshold. An eventually constant function converges to that constant.

      \[\forall R\ge R_0,\ \chi_R(x)=1\quad\Longrightarrow\quad\chi_R(x)\longrightarrow1.\]
      Corresponding Lean step

      apply radialSmoothCutoff_eq_one_of_norm_le hR_pos; linarith

      Lean statement · radialSmoothCutoff_tendsto_one

      `Tendsto ... atTop (𝓝 1)` expresses convergence to the neighborhood filter at one as R grows without bound. It is a pointwise result, and actually follows from exact eventual equality. The source's surrounding Radial section contributes the implicit type E and its NormedAddCommGroup instance; further structures appear explicitly in the declaration.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_tendsto_one (x : E) :
          Tendsto (fun R : ℝ => radialSmoothCutoff R x) atTop (𝓝 1)

      Exact module and namespace context

      Lean proof · radialSmoothCutoff_tendsto_one

      Actual proof outline: Choose a threshold depending on the fixed point: The threshold is allowed to depend on x because x is fixed before the limit is stated. Use eventual equality to obtain the limit: `tendsto_atTop_of_eventually_const` supplies the limit once the remaining equality goal has been proved.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem radialSmoothCutoff_tendsto_one (x : E) :
          Tendsto (fun R : ℝ => radialSmoothCutoff R x) atTop (𝓝 1) := by
        apply tendsto_atTop_of_eventually_const (i₀ := ‖x‖ + 1)
        intro R hR
        have hR_pos : 0 < R := by
          calc
            0 < ‖x‖ + 1 := by positivity
            _ ≤ R := hR
        apply radialSmoothCutoff_eq_one_of_norm_le hR_pos
        linarith
      
      end Radial
      
      section Plateau
      
      /-- A compact subset of an open set admits a smooth compactly supported plateau in that set.
      
      The function takes values in `[0, 1]` and is identically one on the compact set. -/

      Exact module and namespace context

      Scope and omitted-condition boundaries

      • No measure, integrability hypothesis, dominated-convergence argument, or limit of integrals appears.
      • No uniform convergence on all of E is asserted.

      Source and reuse

      ASTIS parents called

      Mathlib API called (external library)

      • tendsto_atTop_of_eventually_const

      Mathematical sources

      ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.

      ASTIS mathematical exposition

      A smooth compactly supported plateau inside any open neighborhood of a compact set

      AutoSamplingTheory.TechnicalLemmas.Analysis.Calculus.Cutoff.exists_contDiff_eq_one_tsupport_subset · theorem · Teaching coverage

      Statement

      Let E be a finite-dimensional real normed vector space. If K⊆E is compact, U⊆E is open, and K⊆U, then there exists a real-valued smooth function χ on E which takes values in [0,1], equals one on K, and has compact topological support contained in U. The statement also explicitly includes ordinary support contained in U.

      \[\begin{gathered}K\text{ compact},\quad U\text{ open},\quad K\subseteq U\\ \Longrightarrow\ \exists\chi\in C^\infty(E,\mathbb R):\quad \operatorname{supp}\chi\subseteq U,\quad\operatorname{tsupp}\chi\subseteq U,\quad\operatorname{tsupp}\chi\text{ compact},\\ 0\le\chi(x)\le1\quad(\forall x\in E),\qquad\chi(x)=1\quad(\forall x\in K).\end{gathered}\]

      All objects and hypotheses

      • E is a normed additive commutative group with a real normed-vector-space structure and is finite-dimensional over ℝ.
      • K and U are subsets of E, K is compact, U is open, and K⊆U.
      • No inner product, nontriviality, or nonemptiness of K or U is assumed.

      Mathematical proof

      1. Insert a compact neighborhood and obtain an auxiliary smooth function

      Choose a compact set L with K⊆interior(L) and L⊆U. Mathlib's smooth-support theorem for an open set supplies a smooth g:E→ℝ with ordinary support exactly interior(L) and all values in [0,1]. Thus g is strictly positive at each point of K.

      \[K\subseteq\operatorname{int}L\subseteq L\subseteq U,\qquad\operatorname{supp}g=\operatorname{int}L,\quad g\in C^\infty,\quad0\le g\le1,\quad g|_K>0.\]
      Corresponding Lean step

      exists_compact_between hK hU hKU; (isOpen_interior : IsOpen (interior L)).exists_contDiff_support_eq (E := E) (n := (⊤ : ℕ∞)); lt_of_le_of_ne (hg_range (Set.mem_range_self x)).1 (Ne.symm hx_support)

      2. Choose a uniform positive lower bound on K

      Continuity and strict positivity of g on the compact set K give a real m>0 such that m≤g(x) for all x∈K. The library statement also handles K empty; no attained positive minimum is claimed in that case.

      \[\exists m>0,\quad\forall x\in K,\quad m\le g(x),\qquad m/2>0.\]
      Corresponding Lean step

      hK.exists_forall_le' hg_contDiff.continuous.continuousOn hg_pos; have hm_half_pos : 0 < m / 2 := half_pos hm_pos

      3. Compress the values through the smooth transition

      Define χ using the standard transition S, with threshold m/2 and transition interval up to m. This composition is smooth because g is smooth and the denominator is a fixed nonzero constant. Its values lie in [0,1] because every value of S does.

      \[\chi(x):=S\!\left(\frac{g(x)-m/2}{m/2}\right),\qquad \chi\in C^\infty(E,\mathbb R),\qquad0\le\chi\le1.\]
      Corresponding Lean step

      let χ : E → ℝ := fun x => Real.smoothTransition ((g x - m / 2) / (m / 2)); Real.smoothTransition.contDiff.comp; (hg_contDiff.sub contDiff_const).div_const (m / 2); ⟨Real.smoothTransition.nonneg _, Real.smoothTransition.le_one _⟩

      4. Separate the nonzero set from the boundary of the auxiliary support

      If χ(x)≠0, then its transition argument must be positive, since S vanishes on nonpositive arguments. Hence g(x)>m/2. In particular the ordinary support lies in the closed superlevel set F={x:g(x)≥m/2}.

      \[\chi(x)\ne0\Longrightarrow\frac{g(x)-m/2}{m/2}>0\Longrightarrow g(x)>m/2,\qquad\operatorname{supp}\chi\subseteq F:=\{x:g(x)\ge m/2\}.\]
      Corresponding Lean step

      Real.smoothTransition.zero_of_nonpos hnonpos; (div_pos_iff_of_pos_right hm_half_pos).mp harg; exact (sub_pos.mp hnum).le

      5. Control the closed support and obtain compactness

      Continuity of g makes F closed, so tsupp χ⊆F. Because m/2>0, every point of F has g(x)≠0, hence lies in interior(L). Therefore tsupp χ⊆L⊆U. The closed set tsupp χ is a subset of compact L and is compact; ordinary support is contained in tsupport and hence also in U.

      \[\operatorname{tsupp}\chi=\overline{\operatorname{supp}\chi}\subseteq F\subseteq\operatorname{int}L\subseteq L\subseteq U,\qquad\operatorname{tsupp}\chi\text{ compact}.\]
      Corresponding Lean step

      isClosed_le continuous_const hg_contDiff.continuous; closure_minimal hχ_support_superlevel hsuperlevel_closed; hm_half_pos.trans_le hx; (subset_tsupport χ).trans hχ_tsupport_U; hL.of_isClosed_subset isClosed_closure hχ_tsupport_L

      6. Verify the plateau and collect all six properties

      For x∈K, the bound g(x)≥m forces the transition argument to be at least one. Hence χ(x)=1. Together with the support inclusions, compactness, smoothness, and range bounds already proved, this supplies the required witness.

      \[x\in K\Longrightarrow \frac{g(x)-m/2}{m/2}\ge1\Longrightarrow\chi(x)=1.\]
      Corresponding Lean step

      apply Real.smoothTransition.one_of_one_le; rw [one_le_div hm_half_pos]; linarith [hm_le x hx]; exact ⟨χ, hχ_support_U, hχ_tsupport_U, hχ_compact, hχ_contDiff, hχ_range, hχ_one⟩

      Lean statement · exists_contDiff_eq_one_tsupport_subset

      This is an existence theorem proved by constructing a witness from two Mathlib existence results and a scalar transition. It is not a definition of a canonical χ: the chosen L, g, and m are not exported as a uniquely determined plateau.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem exists_contDiff_eq_one_tsupport_subset
          {E : Type*} [NormedAddCommGroup E] [NormedSpace ℝ E] [FiniteDimensional ℝ E]
          {K U : Set E} (hK : IsCompact K) (hU : IsOpen U) (hKU : K ⊆ U) :
          ∃ χ : E → ℝ,
            Function.support χ ⊆ U ∧ tsupport χ ⊆ U ∧ HasCompactSupport χ ∧
              ContDiff ℝ (⊤ : ℕ∞) χ ∧ Set.range χ ⊆ Set.Icc 0 1 ∧ Set.EqOn χ 1 K

      Exact module and namespace context

      Lean proof · exists_contDiff_eq_one_tsupport_subset

      Actual proof outline: Insert a compact neighborhood and obtain an auxiliary smooth function: The first two library results provide L and g with their recorded properties. Positivity on K combines nonzero support membership with nonnegativity, rather than assuming g equals one there. Choose a uniform positive lower bound on K: `exists_forall_le'` gives a strict improvement of the lower bound zero, not an extra nonempty-set premise. Compress the values through the smooth transition: This is the construction used to prove the existence theorem. The scalar transition has already been supplied by Mathlib; no new bump formula is postulated. Separate the nonzero set from the boundary of the auxiliary support: The proof weakens the strict lower bound to a non-strict one so that the containing set F is closed and can also contain the closure of the support. Control the closed support and obtain compactness: This is why using a positive superlevel threshold matters: merely knowing supp g⊆U would not by itself control its closure inside U. Verify the plateau and collect all six properties: `Set.EqOn χ 1 K` means equality on K with the constant-one function. The final nested conjunction lists exactly the six requested properties.

      Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

      theorem exists_contDiff_eq_one_tsupport_subset
          {E : Type*} [NormedAddCommGroup E] [NormedSpace ℝ E] [FiniteDimensional ℝ E]
          {K U : Set E} (hK : IsCompact K) (hU : IsOpen U) (hKU : K ⊆ U) :
          ∃ χ : E → ℝ,
            Function.support χ ⊆ U ∧ tsupport χ ⊆ U ∧ HasCompactSupport χ ∧
              ContDiff ℝ (⊤ : ℕ∞) χ ∧ Set.range χ ⊆ Set.Icc 0 1 ∧ Set.EqOn χ 1 K := by
        obtain ⟨L, hL, hK_intL, hL_U⟩ := exists_compact_between hK hU hKU
        obtain ⟨g, hg_support, hg_contDiff, hg_range⟩ :=
          (isOpen_interior : IsOpen (interior L)).exists_contDiff_support_eq
            (E := E) (n := (⊤ : ℕ∞))
        have hg_pos : ∀ x ∈ K, 0 < g x := by
          intro x hx
          have hx_support : x ∈ Function.support g := by
            rw [hg_support]
            exact hK_intL hx
          exact lt_of_le_of_ne
            (hg_range (Set.mem_range_self x)).1 (Ne.symm hx_support)
        obtain ⟨m, hm_pos, hm_le⟩ :=
          hK.exists_forall_le' hg_contDiff.continuous.continuousOn hg_pos
        have hm_half_pos : 0 < m / 2 := half_pos hm_pos
        let χ : E → ℝ := fun x =>
          Real.smoothTransition ((g x - m / 2) / (m / 2))
        have hχ_support_superlevel :
            Function.support χ ⊆ {x : E | m / 2 ≤ g x} := by
          intro x hx
          change χ x ≠ 0 at hx
          have harg : 0 < (g x - m / 2) / (m / 2) := by
            apply lt_of_not_ge
            intro hnonpos
            apply hx
            exact Real.smoothTransition.zero_of_nonpos hnonpos
          have hnum : 0 < g x - m / 2 :=
            (div_pos_iff_of_pos_right hm_half_pos).mp harg
          exact (sub_pos.mp hnum).le
        have hsuperlevel_closed : IsClosed {x : E | m / 2 ≤ g x} :=
          isClosed_le continuous_const hg_contDiff.continuous
        have hχ_tsupport_superlevel :
            tsupport χ ⊆ {x : E | m / 2 ≤ g x} := by
          exact closure_minimal hχ_support_superlevel hsuperlevel_closed
        have hsuperlevel_intL : {x : E | m / 2 ≤ g x} ⊆ interior L := by
          intro x hx
          have hgx_pos : 0 < g x := hm_half_pos.trans_le hx
          have hx_support : x ∈ Function.support g := hgx_pos.ne'
          rwa [hg_support] at hx_support
        have hχ_tsupport_L : tsupport χ ⊆ L :=
          hχ_tsupport_superlevel.trans (hsuperlevel_intL.trans interior_subset)
        have hχ_tsupport_U : tsupport χ ⊆ U := hχ_tsupport_L.trans hL_U
        have hχ_support_U : Function.support χ ⊆ U :=
          (subset_tsupport χ).trans hχ_tsupport_U
        have hχ_compact : HasCompactSupport χ := by
          rw [hasCompactSupport_def]
          exact hL.of_isClosed_subset isClosed_closure hχ_tsupport_L
        have hχ_contDiff : ContDiff ℝ (⊤ : ℕ∞) χ := by
          apply Real.smoothTransition.contDiff.comp
          exact (hg_contDiff.sub contDiff_const).div_const (m / 2)
        have hχ_range : Set.range χ ⊆ Set.Icc (0 : ℝ) 1 := by
          rintro _ ⟨x, rfl⟩
          exact ⟨Real.smoothTransition.nonneg _, Real.smoothTransition.le_one _⟩
        have hχ_one : Set.EqOn χ 1 K := by
          intro x hx
          apply Real.smoothTransition.one_of_one_le
          rw [one_le_div hm_half_pos]
          linarith [hm_le x hx]
        exact ⟨χ, hχ_support_U, hχ_tsupport_U, hχ_compact,
          hχ_contDiff, hχ_range, hχ_one⟩
      
      end Plateau
      
      end Cutoff
      end Calculus
      end Analysis
      end TechnicalLemmas
      end AutoSamplingTheory

      Exact module and namespace context

      Scope and omitted-condition boundaries

      • There are no radius-dependent derivative constants, Hessian estimates, or measure-theoretic limits in this theorem.
      • This proof does not call the radial cutoff family; its compact-within-open construction is a separate API.

      Source and reuse

      ASTIS parents called

        Mathlib API called (external library)

        • exists_compact_between
        • IsOpen.exists_contDiff_support_eq
        • IsCompact.exists_forall_le'
        • Real.smoothTransition
        • Real.smoothTransition.contDiff
        • Real.smoothTransition.nonneg
        • Real.smoothTransition.le_one
        • Real.smoothTransition.zero_of_nonpos
        • Real.smoothTransition.one_of_one_le
        • isClosed_le
        • closure_minimal
        • subset_tsupport
        • IsCompact.of_isClosed_subset

        Mathematical sources

        ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.