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Lemma 14.1: for w⁽¹⁾ = 0 and w⁽ᵗ⁺¹⁾ = w⁽ᵗ⁾ − ηvₜ, ∑ₜ⟨w⁽ᵗ⁾ − w⋆, vₜ⟩ ≤ ‖w⋆‖²/(2η) + (η/2)∑ₜ‖vₜ‖²; with ‖vₜ‖ ≤ ρ, ‖w⋆‖ ≤ B and η = B/(ρ√T) the average is ≤ Bρ/√T

Proved
UnderstandingML.gd_lemma

by naimengye · Sep 24, 2026 · Mathlib 0df444a (Lean v4.33.1)

convex-optimizationgradient-descentregret

Lemma 14.1. Let v1,…,vTv_1, \dots, v_Tv1​,…,vT​ be an arbitrary sequence of vectors. Any algorithm with an initialization w(1)=0w^{(1)} = 0w(1)=0 and an update rule of the form w(t+1)=w(t)−ηvtw^{(t+1)} = w^{(t)} - \eta v_tw(t+1)=w(t)−ηvt​ (14.4) satisfies

∑t=1T⟨w(t)−w⋆,vt⟩≤∥w⋆∥22η+η2∑t=1T∥vt∥2.(14.5)\sum_{t=1}^T \langle w^{(t)} - w^\star, v_t\rangle \le \frac{\|w^\star\|^2}{2\eta} + \frac\eta2\sum_{t=1}^T \|v_t\|^2. \tag{14.5}t=1∑T​⟨w(t)−w⋆,vt​⟩≤2η∥w⋆∥2​+2η​t=1∑T​∥vt​∥2.(14.5)

In particular, for every B,ρ>0B, \rho > 0B,ρ>0, if for all ttt we have ∥vt∥≤ρ\|v_t\| \le \rho∥vt​∥≤ρ and if we set η=B2/(ρ2T)\eta = \sqrt{B^2/(\rho^2 T)}η=B2/(ρ2T)​, then for every w⋆w^\starw⋆ with ∥w⋆∥≤B\|w^\star\| \le B∥w⋆∥≤B we have 1T∑t=1T⟨w(t)−w⋆,vt⟩≤BρT\frac1T\sum_{t=1}^T \langle w^{(t)} - w^\star, v_t\rangle \le \frac{B\rho}{\sqrt T}T1​∑t=1T​⟨w(t)−w⋆,vt​⟩≤T​Bρ​.

Preamble
import Definitions.Def_UnderstandingML_SGD

open MeasureTheory
open scoped InnerProductSpace
Formal statement
namespace UnderstandingML

/-- **Lemma 14.1** (p. 187). Let `v₁, …, v_T` be an arbitrary sequence of vectors. Any algorithm
with an initialization `w⁽¹⁾ = 0` and an update rule `w⁽ᵗ⁺¹⁾ = w⁽ᵗ⁾ − η vₜ` (14.4) satisfies
`∑ₜ ⟨w⁽ᵗ⁾ − w⋆, vₜ⟩ ≤ ‖w⋆‖²/(2η) + (η/2) ∑ₜ ‖vₜ‖²` (14.5). In particular, for every `B, ρ > 0`,
if `‖vₜ‖ ≤ ρ` for all `t` and `η = √(B²/(ρ²T))`, then for every `w⋆` with `‖w⋆‖ ≤ B`,
`(1/T) ∑ₜ ⟨w⁽ᵗ⁾ − w⋆, vₜ⟩ ≤ Bρ/√T`. Iterates indexed from `0`. -/
theorem gd_lemma {d : ℕ} (v : ℕ → Vec d) (T : ℕ) (wstar : Vec d) :
    (∀ η : ℝ, 0 < η →
      ∑ t ∈ Finset.range T, ⟪gdIterates η v t - wstar, v t⟫_ℝ ≤
        ‖wstar‖ ^ 2 / (2 * η) + η / 2 * ∑ t ∈ Finset.range T, ‖v t‖ ^ 2) ∧
    ∀ B ρ : ℝ, 0 < B → 0 < ρ → (∀ t < T, ‖v t‖ ≤ ρ) → ‖wstar‖ ≤ B → 0 < T →
      (∑ t ∈ Finset.range T, ⟪gdIterates (B / (ρ * Real.sqrt T)) v t - wstar, v t⟫_ℝ) / T ≤
        B * ρ / Real.sqrt T := by sorry

end UnderstandingML
Source
Shalev-Shwartz and Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014, doi:10.1017/CBO9781107298019, §14.1.1 p. 187, Lemma 14.1 with its proof
Human review
  • Endorsed by Shuze Chen · Sep 25, 2026

    Confirmed by the moderator at approval.

  • Endorsed by naimengye · Sep 25, 2026

    Confirmed by the mission captain (proposal self-audit).

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