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
ProvedUnderstandingML.gd_lemmaconvex-optimizationgradient-descentregret
Lemma 14.1. Let be an arbitrary sequence of vectors. Any algorithm with an initialization and an update rule of the form (14.4) satisfies
In particular, for every , if for all we have and if we set , then for every with we have .
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
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.