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Theorem 5.3, p. 320 — from ‖x₀ − x*‖ ≤ μ/(2M), Newton's method is well defined and ‖x_{k+1} − x*‖ ≤ (M/μ)‖x_k − x*‖²

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ConvexOptAlg.Newton.theorem_5_3

by mikedeng1 · Oct 5, 2026 · Mathlib 0df444a (Lean v4.33.1)

convergence-rateconvex-optimizationnewton-methodp2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-bookp2o-v1quadratic-convergence

Let f:Rn→Rf:\mathbb R^n\to\mathbb Rf:Rn→R be a C2C^2C2 function whose Hessian is MMM-Lipschitz in operator norm, ∥∇2f(x)−∇2f(y)∥≤M∥x−y∥\|\nabla^2 f(x)-\nabla^2 f(y)\|\le M\|x-y\|∥∇2f(x)−∇2f(y)∥≤M∥x−y∥ for all x,yx,yx,y, with M>0M>0M>0. Let x∗x^*x∗ be a local minimum of fff with strictly positive Hessian, ∇2f(x∗)⪰μIn\nabla^2 f(x^*)\succeq\mu I_n∇2f(x∗)⪰μIn​ for some μ>0\mu>0μ>0. Suppose the starting point x0x_0x0​ satisfies

∥x0−x∗∥≤μ2M.\|x_0-x^*\|\le\frac{\mu}{2M}.∥x0​−x∗∥≤2Mμ​.

Then Newton's method started at x0x_0x0​ is well defined: there is exactly one sequence (xk)k≥0(x_k)_{k\ge0}(xk​)k≥0​ with first term x0x_0x0​ satisfying ∇2f(xk)(xk−xk+1)=∇f(xk)\nabla^2 f(x_k)(x_k-x_{k+1})=\nabla f(x_k)∇2f(xk​)(xk​−xk+1​)=∇f(xk​) for all kkk, and along it every Hessian ∇2f(xk)\nabla^2 f(x_k)∇2f(xk​) is invertible, so that xk+1=xk−[∇2f(xk)]−1∇f(xk)x_{k+1}=x_k-[\nabla^2 f(x_k)]^{-1}\nabla f(x_k)xk+1​=xk​−[∇2f(xk​)]−1∇f(xk​). Moreover the iterates converge to x∗x^*x∗ at a quadratic rate:

∥xk+1−x∗∥≤Mμ ∥xk−x∗∥2for all k≥0,xk→x∗.\|x_{k+1}-x^*\|\le\frac M\mu\,\|x_k-x^*\|^2\qquad\text{for all }k\ge0,\qquad x_k\to x^* .∥xk+1​−x∗∥≤μM​∥xk​−x∗∥2for all k≥0,xk​→x∗.

This is the classical local quadratic convergence of Newton's method: close enough to a nondegenerate local minimum, the number of correct digits roughly doubles at every step. It is the starting point of the interior point methods of §5.3, which keep Newton's method inside such a region of fast convergence.

Formalization Note Rn\mathbb R^nRn is EuclideanSpace ℝ (Fin n); the gradient and Hessian are explicit maps tied to fff (definition item), and the Hessian condition at x∗x^*x∗ is the quadratic-form inequality ⟨∇2f(x∗)v,v⟩≥μ∥v∥2\langle\nabla^2 f(x^*)v,v\rangle\ge\mu\|v\|^2⟨∇2f(x∗)v,v⟩≥μ∥v∥2. "Well-defined" is formalized as unique existence of a Newton run from x0x_0x0​ together with invertibility (bijectivity) of every Hessian along every such run; the quadratic rate and the convergence are asserted for every Newton run from x0x_0x0​. M>0M>0M>0 is a disclosed implicit hypothesis (the radius μ/(2M)\mu/(2M)μ/(2M) divides by MMM; with M=0M=0M=0 Lean's μ/0=0\mu/0=0μ/0=0 would force x0=x∗x_0=x^*x0​=x∗). Convexity of fff is not assumed, as on the page.

Preamble
import Mathlib
import Definitions.Def_ConvexOptAlg_Newton_Defs
Formal statement
namespace ConvexOptAlg.Newton

open Filter Topology

/-- **Theorem 5.3** (Bubeck, arXiv:1405.4980v2, p. 320). Let `f : ℝⁿ → ℝ` be C² with gradient map
`g` and Hessian map `H`, and assume the Hessian is `M`-Lipschitz in operator norm. Let `x∗` be a
local minimum of `f` with `∇²f(x∗) ⪰ μ Iₙ`, `μ > 0`, i.e. `μ‖v‖² ≤ ⟪∇²f(x∗) v, v⟫` for all `v`.
If `‖x₀ − x∗‖ ≤ μ/(2M)`, then Newton's method started at `x₀` is well-defined — there is exactly one
Newton run from `x₀`, and along every Newton run from `x₀` each Hessian `∇²f(x_k)` is invertible —
and it converges to `x∗` at a quadratic rate: `‖x_{k+1} − x∗‖ ≤ (M/μ)‖x_k − x∗‖²` for every `k ≥ 0`.
`0 < M` is a disclosed implicit hypothesis (the radius `μ/(2M)` divides by `M`). -/
theorem theorem_5_3 {n : ℕ} (f : EuclideanSpace ℝ (Fin n) → ℝ)
    (g : EuclideanSpace ℝ (Fin n) → EuclideanSpace ℝ (Fin n))
    (H : EuclideanSpace ℝ (Fin n) → (EuclideanSpace ℝ (Fin n) →L[ℝ] EuclideanSpace ℝ (Fin n)))
    (hfgH : IsC2GradHess f g H) (M μ : ℝ) (hM : 0 < M) (hHL : IsLipschitzHessian H M)
    (xstar : EuclideanSpace ℝ (Fin n)) (hmin : IsLocalMin f xstar) (hμ : 0 < μ)
    (hHstar : ∀ v : EuclideanSpace ℝ (Fin n), μ * ‖v‖ ^ 2 ≤ inner ℝ (H xstar v) v)
    (x0 : EuclideanSpace ℝ (Fin n)) (hx0 : ‖x0 - xstar‖ ≤ μ / (2 * M)) :
    (∃! x : ℕ → EuclideanSpace ℝ (Fin n), x 0 = x0 ∧ IsNewtonRun g H x) ∧
    ∀ x : ℕ → EuclideanSpace ℝ (Fin n), x 0 = x0 → IsNewtonRun g H x →
      (∀ k, Function.Bijective (H (x k))) ∧
      (∀ k, ‖x (k + 1) - xstar‖ ≤ M / μ * ‖x k - xstar‖ ^ 2) ∧
      Tendsto x atTop (𝓝 xstar) := by sorry

end ConvexOptAlg.Newton
Source
Bubeck, Convex Optimization: Algorithms and Complexity, arXiv:1405.4980v2, Theorem 5.3, p. 320

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