Eq. (39) — at a cluster point
ProvedNonconvexSplitting.ADMMKL.eq39_lagrangian_limitadmmnonconvex-optimizationp2o-batch-p200bp2o-gran-per-chapterp2o-plan-paperp2o-v1
Let be twice continuously differentiable with bounded Hessian, proper and closed, linear and , with augmented Lagrangian . Suppose Assumption 1 holds with (hence ), with witnesses , and let be generated by the ADMM
If is a cluster point of , then
The limit value is the level at which the KL inequality is applied.
Formalization Note The ADMM is the relation IsProxADMMSeq with (the Bregman term vanishes); Assumption 1 is instantiated with and , and is left as a parameter (Assumption 1 forces ). Values of and live in EReal; iterates are indexed from , and is not constrained. The limit is taken in EReal. "Cluster point" means the limit of a subsequence with strictly increasing.
Preamble
import Mathlib import Definitions.Def_NonconvexSplitting_Shared_StandingAssumptions import Definitions.Def_NonconvexSplitting_Shared_Assumption1 import Definitions.Def_NonconvexSplitting_Shared_IsProxADMMSeq open NonconvexSplitting.Shared open Filter Topology open scoped InnerProductSpace
Formal statement
namespace NonconvexSplitting.ADMMKL
/-- Eq. (39) of Li–Pong (p. 14): along the ADMM under Assumption 1 with `T₁ = 0`, if
`(x*, y*, z*)` is a cluster point then `L_β(x^t, y^t, z^t) → L_β(x*, y*, z*)` (in `EReal`). -/
theorem eq39_lagrangian_limit {n m : ℕ} (h : EuclideanSpace ℝ (Fin n) → ℝ) (P : EuclideanSpace ℝ (Fin m) → EReal)
(M : EuclideanSpace ℝ (Fin n) →L[ℝ] EuclideanSpace ℝ (Fin m)) (β : ℝ)
(hstd : StandingAssumptions h P (fun _ => 0) β)
(σ δ γ : ℝ) (Q1 Q2 T2 Q3 : EuclideanSpace ℝ (Fin n) →L[ℝ] EuclideanSpace ℝ (Fin n))
(hA : Assumption1 h (fun _ => 0) M β σ Q1 Q2 0 T2 δ Q3 γ)
(x : ℕ → EuclideanSpace ℝ (Fin n)) (y z : ℕ → EuclideanSpace ℝ (Fin m))
(hseq : IsProxADMMSeq h P (fun _ => 0) M β x y z)
(xs : EuclideanSpace ℝ (Fin n)) (ys zs : EuclideanSpace ℝ (Fin m))
(hclu : ∃ s : ℕ → ℕ, StrictMono s ∧
Tendsto (fun i => (x (s i), y (s i), z (s i))) atTop (𝓝 (xs, ys, zs))) :
Tendsto (fun t => augLag h P M β (x t) (y t) (z t)) atTop
(𝓝 (augLag h P M β xs ys zs)) := by sorry
end NonconvexSplitting.ADMMKL
Source
Li & Pong, Global Convergence of Splitting Methods for Nonconvex Composite Optimization, arXiv:1407.0753v6, p. 14, Eq. (39)
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.