The linesearch (2.1) of Algorithm 2.1 terminates
ProvedSolodovSvaiterVI.Alg21.linesearch_well_definedline-searchp2o-batch-p200bp2o-gran-per-chapterp2o-plan-paperp2o-v1projection-methodsvariational-inequalities
Let be a closed convex subset of , continuous, and . Let be the projected residual of . If and , then some nonnegative integer satisfies the linesearch condition
Consequently the smallest such , the index of Algorithm 2.1, exists, and every iteration of the method that has not stopped is well defined.
Formalization Note Stated for a generic point with nonzero residual rather than for an iterate .
Preamble
import Mathlib import Definitions.Def_SolodovSvaiterVI_Alg21_ArmijoHolds
Formal statement
namespace SolodovSvaiterVI.Alg21
/-- Solodov–Svaiter, proof of Theorem 2.1, first paragraph (p. 769): the linesearch of
Algorithm 2.1 is well defined — at a point `x ∈ C` with `r(x) ≠ 0`, condition (2.1) holds for
some nonnegative integer `k`. -/
theorem linesearch_well_defined {n : ℕ}
(F : EuclideanSpace ℝ (Fin n) → EuclideanSpace ℝ (Fin n))
(C : Set (EuclideanSpace ℝ (Fin n))) (hCc : IsClosed C) (hCcv : Convex ℝ C)
(hF : Continuous F) (gamma sigma : ℝ) (hγ0 : 0 < gamma) (hγ1 : gamma < 1)
(hσ0 : 0 < sigma) (hσ1 : sigma < 1)
(x : EuclideanSpace ℝ (Fin n)) (hx : x ∈ C) (hr : residual F C x ≠ 0) :
∃ k : ℕ, ArmijoHolds F C gamma sigma x k := by sorry
end SolodovSvaiterVI.Alg21
Source
Solodov & Svaiter, A New Projection Method for Variational Inequality Problems, SIAM J. Control Optim. 37 (1999), p. 769, proof of Theorem 2.1, first paragraph (Eqs. (2.4)–(2.5))
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.