Recover the chord inequality by integration
AutoSamplingTheory.TechnicalLemmas.Analysis.ConvexityC1.strongConvexOn_univ_of_gradient_mono_integral · theorem · Teaching coverage
Statement
For C¹ f on a complete real inner-product space, a global gradient monotonicity lower bound with any real modulus m implies StrongConvexOn univ m f.
All objects and hypotheses
- E is a complete real inner-product space and f:E→ℝ is continuously Fréchet differentiable on all E.
- gradient f is the genuine gradient supplied by C¹, not an unsupported totalized derivative.
- m∈ℝ may be negative, zero or positive. For all x,y∈E, the displayed gradient lower bound holds.
- The conclusion includes every x,y and every t∈[0,1].
Mathematical proof
1. Compare two segment integrals
Put v=y−x and fix 0≤t≤1. Apply the segment identity to v and to tv. Scalar linearity moves t outside the second integral.
Corresponding Lean step
hy and hz invoke sub_eq_integral_gradient twice; smul_smul, inner_smul_right and integral_const_mul expose A and B.
2. Cancel a strictly positive displacement parameter
The case t=1 is equality. For t<1 and 0<s<1, the difference of the two segment points is s(1−t)v. Apply gradient monotonicity and cancel s(1−t)>0. No division occurs at a zero displacement parameter.
Corresponding Lean step
hdis and hscaled rewrite the gradient hypothesis; le_of_mul_le_mul_left uses mul_pos hs.1 (sub_pos.mpr ht1).
3. Integrate the lower bound
C¹ makes A and B continuous and hence interval integrable. The inequality on the open interval suffices because Lebesgue measure gives its endpoints zero mass. Integrate s from 0 to 1.
Corresponding Lean step
integral_mono_on_of_le_Ioo, Continuous.intervalIntegrable, integral_sub, integral_id and scalar integral linearity.
4. Restore the chord residual
Multiply the integrated lower bound by nonnegative t and substitute the two FTC identities. This gives the exact m/2 correction; it also covers t=0. The signed m is never divided by or required to be positive.
Corresponding Lean step
hpoint matches affine conventions; mul_le_mul_of_nonneg_left and ring arithmetic conclude StrongConvexOn.
Lean statement · strongConvexOn_univ_of_gradient_mono_integral
For C¹ f on a complete real inner-product space, a global gradient monotonicity lower bound with any real modulus m implies StrongConvexOn univ m f.
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 strongConvexOn_univ_of_gradient_mono_integral
{f : E → ℝ} {m : ℝ} (hf : ContDiff ℝ 1 f)
(hm : ∀ x y : E, m * ‖y - x‖ ^ 2 ≤
inner ℝ (gradient f y - gradient f x) (y - x)) :
StrongConvexOn (univ : Set E) m fLean proof · strongConvexOn_univ_of_gradient_mono_integral
Put v=y−x and fix 0≤t≤1. Apply the segment identity to v and to tv. Scalar linearity moves t outside the second integral. The case t=1 is equality. For t<1 and 0<s<1, the difference of the two segment points is s(1−t)v. Apply gradient monotonicity and cancel s(1−t)>0. No division occurs at a zero displacement parameter. C¹ makes A and B continuous and hence interval integrable. The inequality on the open interval suffices because Lebesgue measure gives its endpoints zero mass. Integrate s from 0 to 1. Multiply the integrated lower bound by nonnegative t and substitute the two FTC identities. This gives the exact m/2 correction; it also covers t=0. The signed m is never divided by or required to be positive.
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 strongConvexOn_univ_of_gradient_mono_integral
{f : E → ℝ} {m : ℝ} (hf : ContDiff ℝ 1 f)
(hm : ∀ x y : E, m * ‖y - x‖ ^ 2 ≤
inner ℝ (gradient f y - gradient f x) (y - x)) :
StrongConvexOn (univ : Set E) m f := by
refine ⟨convex_univ, ?_⟩
intro x _ y _ a t ha ht hat
have hat' : a = 1 - t := by linarith
subst a
have ht1 : t ≤ 1 := by linarith
rcases eq_or_lt_of_le ht1 with ht1 | ht1
· subst t
simp
let v := y - x
let A : ℝ → ℝ := fun s => inner ℝ (gradient f (x + s • v)) v
let B : ℝ → ℝ := fun s => inner ℝ (gradient f (x + (s * t) • v)) v
have hA : Continuous A := by
simpa [A, gradient, Function.comp_def] using
((hf.continuous_fderiv (by norm_num)).comp
(continuous_const.add (continuous_id.smul continuous_const))).clm_apply continuous_const
have hB : Continuous B := by
simpa [B, gradient, Function.comp_def] using
((hf.continuous_fderiv (by norm_num)).comp
(continuous_const.add ((continuous_id.mul continuous_const).smul continuous_const))).clm_apply continuous_const
have hbound : ∫ s : ℝ in 0..1, m * s * (1 - t) * ‖v‖ ^ 2 ≤
∫ s : ℝ in 0..1, (A s - B s) := by
apply intervalIntegral.integral_mono_on_of_le_Ioo (by norm_num)
((show Continuous (fun s : ℝ => m * s * (1 - t) * ‖v‖ ^ 2) by fun_prop).intervalIntegrable 0 1)
((hA.sub hB).intervalIntegrable 0 1)
intro s hs
have h := hm (x + (s * t) • v) (x + s • v)
have hdis : (x + s • v) - (x + (s * t) • v) = (s * (1 - t)) • v := by module
rw [hdis, norm_smul, Real.norm_eq_abs, mul_pow, sq_abs, inner_smul_right] at h
have hscaled : (s * (1 - t)) * (m * s * (1 - t) * ‖v‖ ^ 2) ≤
(s * (1 - t)) * inner ℝ
(gradient f (x + s • v) - gradient f (x + (s * t) • v)) v := by nlinarith [h]
have hcancel := le_of_mul_le_mul_left hscaled (mul_pos hs.1 (sub_pos.mpr ht1))
simpa [A, B, inner_sub_left] using hcancel
have hpoly : (∫ s : ℝ in 0..1, m * s * (1 - t) * ‖v‖ ^ 2) =
m / 2 * (1 - t) * ‖v‖ ^ 2 := by
rw [intervalIntegral.integral_mul_const, intervalIntegral.integral_mul_const,
intervalIntegral.integral_const_mul, integral_id]
norm_num only [one_pow, zero_pow, sub_zero]
ring
rw [hpoly, intervalIntegral.integral_sub (hA.intervalIntegrable 0 1)
(hB.intervalIntegrable 0 1)] at hbound
have hy := sub_eq_integral_gradient hf x v
have hz := sub_eq_integral_gradient hf x (t • v)
have hpoint : (1 - t) • x + t • y = x + t • v := by dsimp [v]; module
have hys : x + v = y := by simp [v]
rw [hys] at hy
change f y - f x = ∫ s : ℝ in 0..1, A s at hy
simp only [smul_smul, inner_smul_right, intervalIntegral.integral_const_mul] at hz
change f (x + t • v) - f x = t * ∫ s : ℝ in 0..1, B s at hz
change f ((1 - t) • x + t • y) ≤ (1 - t) * f x + t * f y -
(1 - t) * t * (m / 2 * ‖x - y‖ ^ 2)
rw [hpoint, norm_sub_rev]
change f (x + t • v) ≤ (1 - t) * f x + t * f y -
(1 - t) * t * (m / 2 * ‖v‖ ^ 2)
nlinarith [mul_le_mul_of_nonneg_left hbound ht]
/-- Proposition 1.6, part 1: on all of Euclidean space, the C¹ chord,
quadratic lower-model and gradient-monotonicity conditions are equivalent.
The nonnegative modulus is retained exactly as in the source. -/Scope and omitted-condition boundaries
- This follows the source integral route, generalized to complete real inner-product spaces and signed m. It is distinct from the previously published MVT converse.
- No C²/Hessian characterization or convergence result is claimed.
Source and reuse
ASTIS parents called
Mathlib API called (external library)
- intervalIntegral.integral_mono_on_of_le_Ioo; Continuous.intervalIntegrable
- intervalIntegral.integral_sub, integral_const_mul, integral_mul_const; integral_id
- norm_smul; inner_smul_right; inner_sub_left; le_of_mul_le_mul_left
Mathematical sources
- Sinho Chewi, Proposition 1.6 part 1 — ASTIS-authored statement and explanation of the selected proof boundary.
- Focused mathematical consumers — Reversed segment, signed quadratic, stationary-point growth and zero-dimension tests.
ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.