Eq. (3.19), p. 291 — f(y_s) ≤ min_{x ∈ ℝⁿ} Φ_s(x)
OpenConvexOptAlg.NesterovStrong.eq_3_19accelerated-gradientconvex-optimizationestimate-sequencep2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-bookp2o-v1
Let be -strongly convex and -smooth with , , and let be a run of Nesterov's accelerated gradient descent with the functions of (3.17). Then for every ,
This measures how far below the model can lie: its minimum value is still at least the value of the current iterate . Together with (3.18) it yields the rate of Theorem 3.18.
Formalization Note The minimum is stated without an infimum: the Lean statement says for every . The minimum exists since is a strongly convex quadratic (milestone eq_3_21_form).
Preamble
import Mathlib import Definitions.Def_OnlineConvexOpt_ConvexBasics_StronglyConvexOn import Definitions.Def_ConvexOptAlg_NesterovStrong_Defs open scoped InnerProductSpace
Formal statement
namespace ConvexOptAlg.NesterovStrong
/-- Bubeck, proof of Theorem 3.18, Eq. (3.19), p. 291: for a `β`-smooth, `α`-strongly convex
`f` on `ℝⁿ` and a run `(x, y)` of Nesterov's accelerated gradient descent,
`f(y_s) ≤ min_{z ∈ ℝⁿ} Φ_s(z)` for every `s ≥ 1`, stated as `f(y_s) ≤ Φ_s(z)` for every `z`. -/
theorem eq_3_19 {n : ℕ} (f : EuclideanSpace ℝ (Fin n) → ℝ)
(g : EuclideanSpace ℝ (Fin n) → EuclideanSpace ℝ (Fin n)) (α β : ℝ)
(hα : 0 < α) (hβ : 0 < β)
(hsc : OnlineConvexOpt.ConvexBasics.StronglyConvexOn Set.univ f g α)
(hsm : IsBetaSmooth f g β)
(x y : ℕ → EuclideanSpace ℝ (Fin n)) (hrun : IsNesterovSCRun g α β x y)
(s : ℕ) (hs : 1 ≤ s) (z : EuclideanSpace ℝ (Fin n)) :
f (y s) ≤ Phi f g α β x s z := by sorry
end ConvexOptAlg.NesterovStrong
Source
Bubeck, arXiv:1405.4980v2, proof of Theorem 3.18, Eq. (3.19), p. 291