along a convergent subsequence
ProvedNonconvexSplitting.ProxGrad.P_tendsto_along_subseqnonconvex-optimizationp2o-batch-p200bp2o-gran-per-chapterp2o-plan-paperp2o-v1proximal-gradient
Assume the hypotheses of Theorem 4: the standing assumptions on and ; twice continuously differentiable and convex; with for all ; step size ; and generated by (43). If along a strictly increasing sequence of indices , then
Together with (47), this gives the function-value convergence required by the definition of the limiting subdifferential.
Formalization Note The limit is taken in EReal.
Preamble
import Mathlib import Definitions.Def_NonconvexSplitting_ProxGrad_StandingAssumptions import Definitions.Def_NonconvexSplitting_ProxGrad_HessianSandwich import Definitions.Def_NonconvexSplitting_ProxGrad_IsProxGradSeq open Filter Topology
Formal statement
namespace NonconvexSplitting.ProxGrad
/-- Li–Pong (p. 19): under the hypotheses of Theorem 4, if `x^{t_i} → x*` along a strictly
increasing `t_i`, then `P(x^{t_i + 1}) → P(x*)` (in `EReal`). -/
theorem P_tendsto_along_subseq {n : ℕ} (h q : EuclideanSpace ℝ (Fin n) → ℝ) (P : EuclideanSpace ℝ (Fin n) → EReal) (ℓ β : ℝ)
(x : ℕ → EuclideanSpace ℝ (Fin n))
(hSA : StandingAssumptions h P) (hq : ContDiff ℝ 2 q) (hq_convex : ConvexOn ℝ Set.univ q)
(hℓ : 0 < ℓ) (h44 : HessianSandwich h q ℓ) (hβ : 0 < β)
(hβℓ : β < 1 / ℓ) (hx : IsProxGradSeq h P β x)
(xs : EuclideanSpace ℝ (Fin n)) (φ : ℕ → ℕ) (hφ : StrictMono φ)
(hlim : Tendsto (x ∘ φ) atTop (𝓝 xs)) :
Tendsto (fun i => P (x (φ i + 1))) atTop (𝓝 (P xs)) := by sorry
end NonconvexSplitting.ProxGrad
Source
Li & Pong, Global Convergence of Splitting Methods for Nonconvex Composite Optimization, arXiv:1407.0753v6, p. 19, first paragraph of the page
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.