(9), p. 8 — an L₀-smooth f lies below its quadratic model Q_L(β; α) for every L ≥ L₀
ProvedSubsetShrink.DFO.eq_9descent-lemmafirst-order-methodsp2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-paperp2o-v1
Let be a differentiable convex function whose gradient is -Lipschitz, with :
Then for every and all ,
This is the quadratic upper bound (the "descent lemma") that the DFO method minimizes in place of at each step; it is the first ingredient of the proof of Proposition 2.
Formalization Note Convexity is part of the setting of (7)–(8) ("an -smooth convex function") and is kept as a hypothesis as printed. The gradient is Mathlib's gradient f.
Preamble
import Mathlib import Definitions.Def_SubsetShrink_DFO_Model
Formal statement
namespace SubsetShrink.DFO
/-- (9), p. 8 (Mazumder, Radchenko & Dedieu, arXiv:1708.03288v4): if `f : ℝᵖ → ℝ` is a
differentiable convex function whose gradient is `L₀`-Lipschitz ((8)), then for every
`L ≥ L₀` and all `α, β ∈ ℝᵖ`,
`f(β) ≤ f(α) + ⟨∇f(α), β − α⟩ + (L/2)‖β − α‖₂² = Q_L(β; α)`. -/
theorem eq_9 {p : ℕ} (f : E p → ℝ) (L0 L : ℝ)
(hdiff : Differentiable ℝ f) (hconv : ConvexOn ℝ Set.univ f) (hL0nn : 0 ≤ L0)
(hL0 : ∀ β α : E p, ‖gradient f β - gradient f α‖ ≤ L0 * ‖β - α‖)
(hL : L0 ≤ L) :
∀ β α : E p, f β ≤ QL f L β α := by sorry
end SubsetShrink.DFO
Source
Mazumder, Radchenko & Dedieu, Subset Selection with Shrinkage, arXiv:1708.03288v4, p. 8, (8)–(9)
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.