(4.17) — under (4.8) at every iteration,
ProvedDynSampleSize.Det.eq_4_17gradient-methodsinexact-gradientlinear-convergencep2o-batch-pfp2bp2o-gran-per-chapterp2o-plan-paperp2o-v1
Let satisfy assumption (4.2) with constants , and let be its minimizer, normalized so that . Let , let be arbitrary vectors and let (iteration (4.1) with steplength (4.7)). If the relative-error condition (4.8), , holds at every iteration , then
This is the linear convergence of the function values, from which (4.10), (4.11) and (4.12) of Theorem 4.1 follow.
Formalization Note The paper assumes (4.8) "at every iteration"; the Lean statement assumes it only for the iterations that precede , which is a slightly stronger statement implied by the same proof.
Preamble
import Mathlib import Definitions.Def_DynSampleSize_Det_Setting open Filter Topology
Formal statement
namespace DynSampleSize.Det
theorem eq_4_17 {m : ℕ} (J : EuclideanSpace ℝ (Fin m) → ℝ) (lam L : ℝ)
(hJ : HessianBounds J lam L) (wstar : EuclideanSpace ℝ (Fin m))
(hmin : ∀ w, J wstar ≤ J w) (hJstar : J wstar = 0)
(θ : ℝ) (hθ0 : 0 < θ) (hθ1 : θ < 1) (g w : ℕ → EuclideanSpace ℝ (Fin m))
(hw : ∀ k, w (k + 1) = w k - ((1 - θ) / L) • g k) (k : ℕ)
(h48 : ∀ j < k, ‖g j - gradient J (w j)‖ ≤ θ * ‖g j‖) :
J (w k) ≤ (1 - beta θ * lam / L) ^ k * J (w 0) := by sorry
end DynSampleSize.Det
Source
Byrd, Chin, Nocedal, Wu, Sample size selection in optimization methods for machine learning, Math. Program. (2012), doi:10.1007/s10107-012-0572-5 — authors' version of 18 Oct 2011, p. 9, (4.17)
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.