Section 4.1, Step 2 — converges for every fixed point
ProvedAndersonAccel.Safe.dist_convergesanderson-accelerationfejer-monotonefixed-point-iterationp2o-batch-p200ap2o-gran-per-chapterp2o-plan-paperp2o-v1
Let be nonexpansive and run Algorithm 3.1 with , and max-memory , assuming no iterate is a solution ( for all ). Then for every fixed point the distance converges: there is with
Together with the vanishing residual (4.6), this is the Opial-type ingredient from which convergence of the whole sequence follows.
Formalization Note The hypothesis is the paper's standing simplification at the start of §4.1.
Preamble
import Mathlib import Definitions.Def_AndersonAccel_Safe_Basic import Definitions.Def_AndersonAccel_Safe_IsAAISRun
Formal statement
namespace AndersonAccel.Safe
open Filter Topology
/-- Section 4.1, Step 2 (p. 3181). For every fixed point `y` of `f`, `‖x^k - y‖` converges. -/
theorem dist_converges {n : ℕ} (f : EuclideanSpace ℝ (Fin n) → EuclideanSpace ℝ (Fin n)) (hf : LipschitzWith 1 f)
(θbar τ α D ε : ℝ) (m : ℕ)
(hθ0 : 0 < θbar) (hθ1 : θbar < 1) (hτ0 : 0 < τ) (hτ1 : τ < 1) (hα0 : 0 < α) (hα1 : α < 1)
(hD : 0 < D) (hε : 0 < ε) (hm : 0 < m)
(x xt s y shat ytil : ℕ → EuclideanSpace ℝ (Fin n))
(H : ℕ → (EuclideanSpace ℝ (Fin n) →L[ℝ] EuclideanSpace ℝ (Fin n))) (mem nAA : ℕ → ℕ)
(hrun : IsAAISRun f θbar τ α D ε m x xt s y shat ytil H mem nAA)
(hnosol : ∀ k, f (x k) ≠ x k)
(yf : EuclideanSpace ℝ (Fin n)) (hyf : f yf = yf) :
∃ L : ℝ, Tendsto (fun k => ‖x k - yf‖) atTop (𝓝 L) := by sorry
end AndersonAccel.Safe
Source
Zhang, O'Donoghue, Boyd, SIAM J. Optim. 30 (2020), p. 3181, Section 4.1, Step 2
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.