Samplinglib
Lean gate not recorded for this source state main · 9bb7a7394ab0

HilbertCorrectorBound: mathematical reading route

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. Sharp quadratic corrector bound on a real Hilbert space
Samplinglib mathematical lesson

Sharp quadratic corrector bound on a real Hilbert space

AutoSamplingTheory.TechnicalLemmas.Analysis.HilbertCorrectorBound.quadratic_corrector_bound_of_square_identity · theorem · Teaching coverage

Read this first

Mathematical statement

Let H be any complete real inner-product space, including the zero space. Let K,D:H→H be bounded real-linear selfadjoint operators with I+K²=D². Let c≥0 and ‖D‖≤c. Then for every u,v∈H, |(‖u‖²−‖v‖²)/2−⟨Ku,v⟩|≤(c/2)(‖u‖²+‖v‖²). No finite-dimensionality, nontriviality, c≥1, positivity of K or D, or commutation is assumed. This ASTIS auxiliary Hilbert-space lemma supplies the sharp two-component estimate in the PBPS B23 proof; its actual consumer internally produces K,D,c and all hypotheses.

\[\left|\tfrac12(\|u\|^2-\|v\|^2)-\langle Ku,v\rangle\right|\le\tfrac c2(\|u\|^2+\|v\|^2).\]
Lean statement · quadratic_corrector_bound_of_square_identity

Let H be any complete real inner-product space, including the zero space. Let K,D:H→H be bounded real-linear selfadjoint operators with I+K²=D². Let c≥0 and ‖D‖≤c. Then for every u,v∈H, |(‖u‖²−‖v‖²)/2−⟨Ku,v⟩|≤(c/2)(‖u‖²+‖v‖²). No finite-dimensionality, nontriviality, c≥1, positivity of K or D, or commutation is assumed. This ASTIS auxiliary Hilbert-space lemma supplies the sharp two-component estimate in the PBPS B23 proof; its actual consumer internally produces K,D,c and all hypotheses.

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 quadratic_corrector_bound_of_square_identity
    {H : Type*} [NormedAddCommGroup H] [InnerProductSpace ℝ H] [CompleteSpace H]
    (K D : H →L[ℝ] H) (hK : IsSelfAdjoint K) (hD : IsSelfAdjoint D)
    (hSquare : (1 : H →L[ℝ] H)+K*K=D*D)
    (c : ℝ) (hc : 0 ≤ c) (hNorm : ‖D‖ ≤ c) (u v : H) :
    |(1/2 : ℝ)*(‖u‖^2-‖v‖^2)-inner ℝ (K u) v| ≤
      (c/2)*(‖u‖^2+‖v‖^2)

Exact module and namespace context

Proof roadmap

  1. 1Turn the operator square into a norm identity

    Evaluate I+K²=D² against x. Selfadjointness moves the outer K and D across the inner product.

  2. 2Cancel the two block cross terms

    Use the sum of the two squared component norms. Symmetry cancels the mixed terms; this is the Hilbert sum energy, without a product max-norm assertion.

  3. 3Bound the quadratic form and block energy

    Set S=‖u‖²+‖v‖² and T=‖u-Kv‖²+‖Ku+v‖²=‖Du‖²+‖Dv‖² throughout this five-step proof. Triangle inequality and the inner-product bound control E. The operator norm controls each D component.

  4. 4Apply two-component Cauchy-Schwarz

    The scalar inequality follows from a single nonnegative square. Combine with the block energy bound, keeping S nonnegative.

  5. 5Take the nonnegative square root and divide by two

    Both |E| and cS are nonnegative, so their square inequality gives |E|≤cS. The corrector is E/2.

Full assumptions and notation

All objects and hypotheses

  • Complete real inner-product space H; bounded selfadjoint real operators K,D; I+K²=D²; c≥0 and ‖D‖≤c. Zero H is legal.

Mathematical proof

1. Turn the operator square into a norm identity

Evaluate I+K²=D² against x. Selfadjointness moves the outer K and D across the inner product.

\[\|x\|^2+\|Kx\|^2=\|Dx\|^2.\]
Corresponding Lean step
  have hEnergy (x : H) : ‖x‖^2+‖K x‖^2=‖D x‖^2 := by
    have h := congrArg (fun A : H →L[ℝ] H => inner ℝ (A x) x) hSquare
    simp only [ContinuousLinearMap.add_apply, ContinuousLinearMap.one_apply,
      ContinuousLinearMap.mul_apply, inner_add_left] at h
    have hk : inner ℝ (K (K x)) x=inner ℝ (K x) (K x) :=
      hK.isSymmetric (K x) x
    have hd : inner ℝ (D (D x)) x=inner ℝ (D x) (D x) :=
      hD.isSymmetric (D x) x
    rw [hk, hd] at h
    simpa only [real_inner_self_eq_norm_sq] using h

Literal compiled BODY span; exact preceding locals remain in scope.

2. Cancel the two block cross terms

Use the sum of the two squared component norms. Symmetry cancels the mixed terms; this is the Hilbert sum energy, without a product max-norm assertion.

\[\|u-Kv\|^2+\|Ku+v\|^2=\|Du\|^2+\|Dv\|^2,\quad E=\langle u-Kv,u\rangle-\langle Ku+v,v\rangle.\]
Corresponding Lean step
  have hSym : inner ℝ u (K v)=inner ℝ (K u) v :=
    (hK.isSymmetric u v).symm
  have hBlock : ‖u-K v‖^2+‖K u+v‖^2=‖D u‖^2+‖D v‖^2 := by
    rw [norm_sub_sq_real, norm_add_sq_real, hSym]
    nlinarith only [hEnergy u, hEnergy v]
  have hForm : inner ℝ (u-K v) u-inner ℝ (K u+v) v =
      ‖u‖^2-‖v‖^2-2*inner ℝ (K u) v := by
    have hSwap : inner ℝ (K v) u=inner ℝ (K u) v :=
      (hK.isSymmetric v u).trans (real_inner_comm v (K u)).symm
    rw [inner_sub_left, inner_add_left, hSwap, real_inner_self_eq_norm_sq,
      real_inner_self_eq_norm_sq]
    ring

Literal compiled BODY span; exact preceding locals remain in scope.

3. Bound the quadratic form and block energy

Set S=‖u‖²+‖v‖² and T=‖u-Kv‖²+‖Ku+v‖²=‖Du‖²+‖Dv‖² throughout this five-step proof. Triangle inequality and the inner-product bound control E. The operator norm controls each D component.

\[|E|\le\|u-Kv\|\|u\|+\|Ku+v\|\|v\|,\quad T\le c^2 S.\]
Corresponding Lean step
  have hAbs : |‖u‖^2-‖v‖^2-2*inner ℝ (K u) v| ≤
      ‖u-K v‖*‖u‖+‖K u+v‖*‖v‖ := by
    rw [← hForm]
    exact (abs_sub _ _).trans (add_le_add
      (abs_real_inner_le_norm _ _) (abs_real_inner_le_norm _ _))
  have hDx (x : H) : ‖D x‖^2 ≤ c^2*‖x‖^2 := by
    have hb : ‖D x‖ ≤ c*‖x‖ :=
      (D.le_opNorm x).trans (mul_le_mul_of_nonneg_right hNorm (norm_nonneg x))
    have hs := (sq_le_sq₀ (norm_nonneg (D x)) (mul_nonneg hc (norm_nonneg x))).2 hb
    nlinarith only [hs]
  have hBlockBound : ‖u-K v‖^2+‖K u+v‖^2 ≤ c^2*(‖u‖^2+‖v‖^2) := by
    rw [hBlock]
    nlinarith only [hDx u, hDx v]

Literal compiled BODY span; exact preceding locals remain in scope.

4. Apply two-component Cauchy-Schwarz

The scalar inequality follows from a single nonnegative square. Combine with the block energy bound, keeping S nonnegative.

\[(ab+de)^2\le(a^2+d^2)(b^2+e^2),\quad E^2\le S T\le c^2S^2.\]
Corresponding Lean step
  have hCS : (‖u-K v‖*‖u‖+‖K u+v‖*‖v‖)^2 ≤
      (‖u‖^2+‖v‖^2)*(‖u-K v‖^2+‖K u+v‖^2) := by
    nlinarith only [sq_nonneg (‖u-K v‖*‖v‖-‖K u+v‖*‖u‖)]
  have hSquared : |‖u‖^2-‖v‖^2-2*inner ℝ (K u) v|^2 ≤
      (c*(‖u‖^2+‖v‖^2))^2 := by
    calc
      _ ≤ (‖u-K v‖*‖u‖+‖K u+v‖*‖v‖)^2 :=
        (sq_le_sq₀ (abs_nonneg _) (by positivity)).2 hAbs
      _ ≤ (‖u‖^2+‖v‖^2)*(‖u-K v‖^2+‖K u+v‖^2) := hCS
      _ ≤ (‖u‖^2+‖v‖^2)*(c^2*(‖u‖^2+‖v‖^2)) :=
        mul_le_mul_of_nonneg_left hBlockBound (by positivity)
      _ = _ := by ring

Literal compiled BODY span; exact preceding locals remain in scope.

5. Take the nonnegative square root and divide by two

Both |E| and cS are nonnegative, so their square inequality gives |E|≤cS. The corrector is E/2.

\[|E|\le cS\quad\Longrightarrow\quad\left|\frac{\|u\|^2-\|v\|^2}{2}-\langle Ku,v\rangle\right|\le\frac c2S.\]
Corresponding Lean step
  have hBound := (sq_le_sq₀ (abs_nonneg
    (‖u‖^2-‖v‖^2-2*inner ℝ (K u) v)) (by positivity)).1 hSquared
  have hHalf : (1/2 : ℝ)*(‖u‖^2-‖v‖^2)-inner ℝ (K u) v =
      (1/2 : ℝ)*(‖u‖^2-‖v‖^2-2*inner ℝ (K u) v) := by ring
  rw [hHalf, abs_mul, abs_of_nonneg (by norm_num : (0 : ℝ) ≤ 1/2)]
  nlinarith only [hBound]

Literal compiled BODY span; exact preceding locals remain in scope.

Lean verification

The exact proof begins folded immediately after the mathematical argument; the statement is folded beside the claim above.

Lean proof · quadratic_corrector_bound_of_square_identity

Turn the operator square into a norm identity; Cancel the two block cross terms; Bound the quadratic form and block energy; Apply two-component Cauchy-Schwarz; Take the nonnegative square root and divide by two

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.

Download module (.lean)
theorem quadratic_corrector_bound_of_square_identity
    {H : Type*} [NormedAddCommGroup H] [InnerProductSpace ℝ H] [CompleteSpace H]
    (K D : H →L[ℝ] H) (hK : IsSelfAdjoint K) (hD : IsSelfAdjoint D)
    (hSquare : (1 : H →L[ℝ] H)+K*K=D*D)
    (c : ℝ) (hc : 0 ≤ c) (hNorm : ‖D‖ ≤ c) (u v : H) :
    |(1/2 : ℝ)*(‖u‖^2-‖v‖^2)-inner ℝ (K u) v| ≤
      (c/2)*(‖u‖^2+‖v‖^2)
 := by
  have hEnergy (x : H) : ‖x‖^2+‖K x‖^2=‖D x‖^2 := by
    have h := congrArg (fun A : H →L[ℝ] H => inner ℝ (A x) x) hSquare
    simp only [ContinuousLinearMap.add_apply, ContinuousLinearMap.one_apply,
      ContinuousLinearMap.mul_apply, inner_add_left] at h
    have hk : inner ℝ (K (K x)) x=inner ℝ (K x) (K x) :=
      hK.isSymmetric (K x) x
    have hd : inner ℝ (D (D x)) x=inner ℝ (D x) (D x) :=
      hD.isSymmetric (D x) x
    rw [hk, hd] at h
    simpa only [real_inner_self_eq_norm_sq] using h
  have hSym : inner ℝ u (K v)=inner ℝ (K u) v :=
    (hK.isSymmetric u v).symm
  have hBlock : ‖u-K v‖^2+‖K u+v‖^2=‖D u‖^2+‖D v‖^2 := by
    rw [norm_sub_sq_real, norm_add_sq_real, hSym]
    nlinarith only [hEnergy u, hEnergy v]
  have hForm : inner ℝ (u-K v) u-inner ℝ (K u+v) v =
      ‖u‖^2-‖v‖^2-2*inner ℝ (K u) v := by
    have hSwap : inner ℝ (K v) u=inner ℝ (K u) v :=
      (hK.isSymmetric v u).trans (real_inner_comm v (K u)).symm
    rw [inner_sub_left, inner_add_left, hSwap, real_inner_self_eq_norm_sq,
      real_inner_self_eq_norm_sq]
    ring
  have hAbs : |‖u‖^2-‖v‖^2-2*inner ℝ (K u) v| ≤
      ‖u-K v‖*‖u‖+‖K u+v‖*‖v‖ := by
    rw [← hForm]
    exact (abs_sub _ _).trans (add_le_add
      (abs_real_inner_le_norm _ _) (abs_real_inner_le_norm _ _))
  have hDx (x : H) : ‖D x‖^2 ≤ c^2*‖x‖^2 := by
    have hb : ‖D x‖ ≤ c*‖x‖ :=
      (D.le_opNorm x).trans (mul_le_mul_of_nonneg_right hNorm (norm_nonneg x))
    have hs := (sq_le_sq₀ (norm_nonneg (D x)) (mul_nonneg hc (norm_nonneg x))).2 hb
    nlinarith only [hs]
  have hBlockBound : ‖u-K v‖^2+‖K u+v‖^2 ≤ c^2*(‖u‖^2+‖v‖^2) := by
    rw [hBlock]
    nlinarith only [hDx u, hDx v]
  have hCS : (‖u-K v‖*‖u‖+‖K u+v‖*‖v‖)^2 ≤
      (‖u‖^2+‖v‖^2)*(‖u-K v‖^2+‖K u+v‖^2) := by
    nlinarith only [sq_nonneg (‖u-K v‖*‖v‖-‖K u+v‖*‖u‖)]
  have hSquared : |‖u‖^2-‖v‖^2-2*inner ℝ (K u) v|^2 ≤
      (c*(‖u‖^2+‖v‖^2))^2 := by
    calc
      _ ≤ (‖u-K v‖*‖u‖+‖K u+v‖*‖v‖)^2 :=
        (sq_le_sq₀ (abs_nonneg _) (by positivity)).2 hAbs
      _ ≤ (‖u‖^2+‖v‖^2)*(‖u-K v‖^2+‖K u+v‖^2) := hCS
      _ ≤ (‖u‖^2+‖v‖^2)*(c^2*(‖u‖^2+‖v‖^2)) :=
        mul_le_mul_of_nonneg_left hBlockBound (by positivity)
      _ = _ := by ring
  have hBound := (sq_le_sq₀ (abs_nonneg
    (‖u‖^2-‖v‖^2-2*inner ℝ (K u) v)) (by positivity)).1 hSquared
  have hHalf : (1/2 : ℝ)*(‖u‖^2-‖v‖^2)-inner ℝ (K u) v =
      (1/2 : ℝ)*(‖u‖^2-‖v‖^2-2*inner ℝ (K u) v) := by ring
  rw [hHalf, abs_mul, abs_of_nonneg (by norm_num : (0 : ℝ) ≤ 1/2)]
  nlinarith only [hBound]

end
end AutoSamplingTheory.TechnicalLemmas.Analysis.HilbertCorrectorBound

#print axioms AutoSamplingTheory.TechnicalLemmas.Analysis.HilbertCorrectorBound.quadratic_corrector_bound_of_square_identity

Exact module and namespace context

Scope and omitted-condition boundaries
  • ASTIS sharp quadratic-form auxiliary on an arbitrary complete real Hilbert space; explicit selfadjoint square identity and norm bound. Actual PBPS inputs and these internal hypotheses are supplied by the actual production consumer. No full dynamics, mixing or cost result.
Sources, dependencies, and reuse

Source and reuse

Samplinglib parents called

    Mathlib API called (external library)

    • IsSelfAdjoint.isSymmetric
    • norm_add_sq_real
    • norm_sub_sq_real
    • abs_real_inner_le_norm
    • ContinuousLinearMap.le_opNorm
    • sq_le_sq₀

    Mathematical sources

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