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Theorem 7.3, p. 33 — Semi-AFB (Algorithm 4) has ℒ_{k+1} ≤ ℒ_k/(1 + α_k), (87) and (88)

Proved
NAGFlow.AFB.theorem_7_3

by mikedeng1 · Oct 9, 2026 · Mathlib 0df444a (Lean v4.33.1)

accelerated-gradientcomposite-optimizationconvergence-rateforward-backward-splittinglyapunov-functionp2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-paperp2o-v1

Let VVV be a real Hilbert space. Let Q⊆VQ\subseteq VQ⊆V be closed and convex, let h∈Sμ,L1,1(Q)h\in\mathcal S^{1,1}_{\mu,L}(Q)h∈Sμ,L1,1​(Q) with 0≤μ≤L<∞0\le\mu\le L<\infty0≤μ≤L<∞, and let g:V→R∪{+∞}g:V\to\mathbb R\cup\{+\infty\}g:V→R∪{+∞} be proper, closed and convex with Q∩dom⁡g≠∅Q\cap\operatorname{dom}g\ne\emptysetQ∩domg=∅ (problem (104)). Let x∗x^*x∗ minimise f=h+gf=h+gf=h+g over QQQ. Let (xk,yk,wk,vk)(x_k,y_k,w_k,v_k)(xk​,yk​,wk​,vk​) with parameters (αk,γk)(\alpha_k,\gamma_k)(αk​,γk​) be a run of Algorithm 4 (Semi-AFB) whose initial point x0x_0x0​ lies in dom⁡g\operatorname{dom}gdomg, and let

Lk=f(xk)−f(x∗)+γk2∥vk−x∗∥2.\mathcal L_k=f(x_k)-f(x^*)+\frac{\gamma_k}{2}\|v_k-x^*\|^2.Lk​=f(xk​)−f(x∗)+2γk​​∥vk​−x∗∥2.

Then

  1. for every k∈Nk\in\mathbb Nk∈N,
Lk+1≤Lk1+αk;(121)\mathcal L_{k+1}\le\frac{\mathcal L_k}{1+\alpha_k};\qquad(121)Lk+1​≤1+αk​Lk​​;(121)
  1. for every k≥0k\ge0k≥0,
Lk≤L0×min⁡{4L(γ0 k+2L)2, (1+min⁡{γ0,μ}L)−k};(87)\mathcal L_k\le\mathcal L_0\times\min\left\{\frac{4L}{(\sqrt{\gamma_0}\,k+2\sqrt L)^2},\ \left(1+\sqrt{\frac{\min\{\gamma_0,\mu\}}{L}}\right)^{-k}\right\};\qquad(87)Lk​≤L0​×min⎩⎨⎧​(γ0​​k+2L​)24L​, (1+Lmin{γ0​,μ}​​)−k⎭⎬⎫​;(87)
  1. for every k≥1k\ge1k≥1,
Lk≤Cγ0,L×min⁡{4k2, (1+min⁡{γ0,μ}L)1−k},(88)\mathcal L_k\le C_{\gamma_0,L}\times\min\left\{\frac{4}{k^2},\ \left(1+\sqrt{\frac{\min\{\gamma_0,\mu\}}{L}}\right)^{1-k}\right\},\qquad(88)Lk​≤Cγ0​,L​×min⎩⎨⎧​k24​, (1+Lmin{γ0​,μ}​​)1−k⎭⎬⎫​,(88)

where

Cγ0,L=Lγ0(f(x0)−f(x∗))+L2∥v0−x∗∥2.(89)C_{\gamma_0,L}=\frac{L}{\gamma_0}\bigl(f(x_0)-f(x^*)\bigr)+\frac{L}{2}\|v_0-x^*\|^2.\qquad(89)Cγ0​,L​=γ0​L​(f(x0​)−f(x∗))+2L​∥v0​−x∗∥2.(89)

Semi-AFB treats hhh explicitly and ggg implicitly, keeps all iterates in QQQ, and needs one proximal computation of ggg over QQQ per iteration. The theorem gives it the accelerated rate O(L/k2)O(L/k^2)O(L/k2) for μ=0\mu=0μ=0 and the accelerated linear rate (1+min⁡{γ0,μ}/L)−k(1+\sqrt{\min\{\gamma_0,\mu\}/L})^{-k}(1+min{γ0​,μ}/L​)−k for μ>0\mu>0μ>0, in one statement, for constrained composite problems where ∇h\nabla h∇h need not exist outside QQQ.

Formalization Note. V∗V^*V∗ is identified with VVV (Riesz). ggg is encoded by its domain DDD and its finite values on DDD (see the definition file); step 5's argmin over QQQ is a minimiser over Q∩DQ\cap DQ∩D. The hypothesis x0∈dom⁡gx_0\in\operatorname{dom}gx0​∈domg is added to the page's input x0,v0∈Qx_0,v_0\in Qx0​,v0​∈Q: without it L0=Cγ0,L=+∞\mathcal L_0=C_{\gamma_0,L}=+\inftyL0​=Cγ0​,L​=+∞. The positivity γ0>0\gamma_0>0γ0​>0, αk>0\alpha_k>0αk​>0 and L>0L>0L>0 are the inputs of Algorithm 4; αk\alpha_kαk​ is any positive solution of Lαk2=γk(1+αk)L\alpha_k^2=\gamma_k(1+\alpha_k)Lαk2​=γk​(1+αk​). No subgradient or variational-inequality hypothesis is made. When μ=0\mu=0μ=0 the second entry of each minimum equals 111.

Preamble
import Mathlib
import Definitions.Def_NAGFlow_AFB_Setting
Formal statement
namespace NAGFlow.AFB

/-- Theorem 7.3 (Luo & Chen, arXiv:1909.03145v4, p. 33), with (87)–(89) of p. 21. Let `Q` be closed
and convex, `h ∈ S^{1,1}_{μ,L}(Q)` with `0 ≤ μ ≤ L < ∞`, `g` proper, closed and convex with domain `D`,
`Q ∩ D ≠ ∅` (problem (104)), and let `x*` minimise `f = h + g` over `Q`. For every run of Algorithm 4
whose initial point `x₀` lies in `dom g`, with `ℒ_k = f(x_k) − f(x*) + (γ_k/2)‖v_k − x*‖²`:
(121) `ℒ_{k+1} ≤ ℒ_k/(1 + α_k)` for every `k ∈ ℕ`;
(87) `ℒ_k ≤ ℒ₀ · min{4L/(√γ₀ k + 2√L)², (1 + √(min{γ₀, μ}/L))^{−k}}` for every `k ≥ 0`;
(88) `ℒ_k ≤ C_{γ₀,L} · min{4/k², (1 + √(min{γ₀, μ}/L))^{1−k}}` for every `k ≥ 1`, where
(89) `C_{γ₀,L} = (L/γ₀)(f(x₀) − f(x*)) + (L/2)‖v₀ − x*‖²`. -/
theorem theorem_7_3 {V : Type*} [NormedAddCommGroup V] [InnerProductSpace ℝ V] [CompleteSpace V]
    (h : V → ℝ) (gradh : V → V) (g : V → ℝ) (D Q : Set V) (μ L : ℝ)
    (hprob : IsAFBProblem h gradh g D Q μ L)
    (α γ : ℕ → ℝ) (x y w v : ℕ → V) (hrun : IsAFBRun gradh g D Q μ L α γ x y w v)
    (xstar : V) (hxstar : IsMinimizer h g D Q xstar) (hx0D : x 0 ∈ D) :
    (∀ k : ℕ, lyap h g xstar x v γ (k + 1) ≤ lyap h g xstar x v γ k / (1 + α k)) ∧
      (∀ k : ℕ, lyap h g xstar x v γ k ≤
        lyap h g xstar x v γ 0 *
          min (4 * L / (Real.sqrt (γ 0) * k + 2 * Real.sqrt L) ^ 2)
            ((1 + Real.sqrt (min (γ 0) μ / L)) ^ (-(k : ℤ)))) ∧
      (∀ k : ℕ, 1 ≤ k → lyap h g xstar x v γ k ≤
        (L / γ 0 * (fObj h g (x 0) - fObj h g xstar) + L / 2 * ‖v 0 - xstar‖ ^ 2) *
          min (4 / (k : ℝ) ^ 2) ((1 + Real.sqrt (min (γ 0) μ / L)) ^ (1 - (k : ℤ)))) := by sorry

end NAGFlow.AFB
Source
Luo & Chen, arXiv:1909.03145v4, Theorem 7.3 and (121), p. 33; (87)–(89), p. 21
Human review
  • Endorsed by Shuze Chen · Oct 10, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Oct 10, 2026

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

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