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Eq. (39) — Lβ(xt,yt,zt)→Lβ(x∗,y∗,z∗)L_\beta(x^t,y^t,z^t)\to L_\beta(x^*,y^*,z^*)Lβ​(xt,yt,zt)→Lβ​(x∗,y∗,z∗) at a cluster point

Proved
NonconvexSplitting.ADMMKL.eq39_lagrangian_limit

by mikedeng1 · Sep 29, 2026 · Mathlib 0df444a (Lean v4.33.1)

admmnonconvex-optimizationp2o-batch-p200bp2o-gran-per-chapterp2o-plan-paperp2o-v1

Let h:Rn→Rh:\mathbb R^n\to\mathbb Rh:Rn→R be twice continuously differentiable with bounded Hessian, P:Rm→(−∞,+∞]P:\mathbb R^m\to(-\infty,+\infty]P:Rm→(−∞,+∞] proper and closed, M:Rn→Rm\mathcal M:\mathbb R^n\to\mathbb R^mM:Rn→Rm linear and β>0\beta>0β>0, with augmented Lagrangian Lβ(x,y,z)=h(x)+P(y)−⟨z,Mx−y⟩+β2∥Mx−y∥2L_\beta(x,y,z)=h(x)+P(y)-\langle z,\mathcal Mx-y\rangle+\frac\beta2\|\mathcal Mx-y\|^2Lβ​(x,y,z)=h(x)+P(y)−⟨z,Mx−y⟩+2β​∥Mx−y∥2. Suppose Assumption 1 holds with T1=0\mathcal T_1=0T1​=0 (hence ϕ=0\phi=0ϕ=0), with witnesses σ,Q1,Q2,δ,Q3,γ\sigma,\mathcal Q_1,\mathcal Q_2,\delta,\mathcal Q_3,\gammaσ,Q1​,Q2​,δ,Q3​,γ, and let (xt,yt,zt)t≥0(x^t,y^t,z^t)_{t\ge0}(xt,yt,zt)t≥0​ be generated by the ADMM

yt+1∈Arg min⁡yLβ(xt,y,zt),xt+1∈Arg min⁡xLβ(x,yt+1,zt),zt+1=zt−β(Mxt+1−yt+1).y^{t+1}\in\operatorname*{Arg\,min}_y L_\beta(x^t,y,z^t),\qquad x^{t+1}\in\operatorname*{Arg\,min}_x L_\beta(x,y^{t+1},z^t),\qquad z^{t+1}=z^t-\beta(\mathcal Mx^{t+1}-y^{t+1}).yt+1∈yArgmin​Lβ​(xt,y,zt),xt+1∈xArgmin​Lβ​(x,yt+1,zt),zt+1=zt−β(Mxt+1−yt+1).

If (x∗,y∗,z∗)(x^*,y^*,z^*)(x∗,y∗,z∗) is a cluster point of (xt,yt,zt)(x^t,y^t,z^t)(xt,yt,zt), then

lim⁡t→∞Lβ(xt,yt,zt)=l∗:=Lβ(x∗,y∗,z∗).\lim_{t\to\infty}L_\beta(x^t,y^t,z^t)=l^*:=L_\beta(x^*,y^*,z^*).t→∞lim​Lβ​(xt,yt,zt)=l∗:=Lβ​(x∗,y∗,z∗).

The limit value l∗l^*l∗ is the level at which the KL inequality is applied.

Formalization Note The ADMM is the relation IsProxADMMSeq with ϕ=0\phi = 0ϕ=0 (the Bregman term vanishes); Assumption 1 is instantiated with ϕ=0\phi = 0ϕ=0 and T1=0\mathcal T_1 = 0T1​=0, and T2\mathcal T_2T2​ is left as a parameter (Assumption 1 forces 0⪯T2⪯T1=00 \preceq \mathcal T_2 \preceq \mathcal T_1 = 00⪯T2​⪯T1​=0). Values of LβL_\betaLβ​ and PPP live in EReal; iterates are indexed from 000, and y0y^0y0 is not constrained. The limit is taken in EReal. "Cluster point" means the limit of a subsequence (xs(i),ys(i),zs(i))(x^{s(i)},y^{s(i)},z^{s(i)})(xs(i),ys(i),zs(i)) with sss 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
  • Endorsed by Shuze Chen · Oct 1, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Oct 1, 2026

    Confirmed by the mission captain (proposal self-audit).

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