Theorem 2.3 —
ProvedCalamaiMore.Convergence.step_ratio_tendsto_zeroconvergencegradient-projectionp2o-batch-p200ap2o-gran-per-chapterp2o-plan-paperp2o-v1
Let be a nonempty closed convex subset of a finite-dimensional real inner product space , let be continuously differentiable on , and let be a run of the gradient projection method defined by (2.1) and (2.2), with constants and . If is bounded below on and is uniformly continuous on , then
No boundedness of the iterates or of the steps is assumed. The ratio is a lower bound for , and the theorem is the main step towards the convergence of the projected gradients (Theorem 3.2).
Preamble
import Mathlib import Definitions.Def_CalamaiMore_Convergence_IsGradientProjectionRun
Formal statement
namespace CalamaiMore.Convergence
/-- Calamai–Moré, Theorem 2.3 (p. 99): if `f` is bounded below on `Ω` and `∇f` is uniformly
continuous on `Ω`, then `‖x_{k+1} - x_k‖ / α_k → 0`. -/
theorem step_ratio_tendsto_zero {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E] [FiniteDimensional ℝ E]
(Ω : Set E) (hΩne : Ω.Nonempty) (hΩc : IsClosed Ω) (hΩcv : Convex ℝ Ω)
(f : E → ℝ) (hfd : ∀ x ∈ Ω, DifferentiableAt ℝ f x) (hfc : ContinuousOn (gradient f) Ω)
(γ₁ γ₂ μ₁ μ₂ : ℝ) (hγ₁ : 0 < γ₁) (hγ₂ : 0 < γ₂)
(hμ₁ : μ₁ ∈ Set.Ioo (0 : ℝ) 1) (hμ₂ : μ₂ ∈ Set.Ioo (0 : ℝ) 1)
(x : ℕ → E) (α : ℕ → ℝ) (hrun : IsGradientProjectionRun f Ω γ₁ γ₂ μ₁ μ₂ x α)
(hbdd : BddBelow (f '' Ω)) (huc : UniformContinuousOn (gradient f) Ω) :
Filter.Tendsto (fun k => ‖x (k + 1) - x k‖ / α k) Filter.atTop (nhds 0) := by sorry
end CalamaiMore.Convergence
Source
Calamai & Moré, Projected gradient methods for linearly constrained problems, Math. Programming 39 (1987), p. 99, Theorem 2.3
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.