Feasibility of a penalty cluster point
ProvedVectorSpaceOpt.penalty_cluster_point_feasibleLet x₀ be the limit of a subsequence x ∘ phi, where phi is strictly increasing. Suppose the scalar constraint violation v is lower semicontinuous at x₀ and v (x n) tends to zero along the original sequence. Then x₀ is feasible:
Lower semicontinuity forces v x₀ ≤ 0, while the finite sum-of-squares definition is nonnegative and vanishes exactly when every positive part is zero. The theorem isolates the feasibility half of §10.11, Theorem 1 and makes the source's phrase “limit point” precise through an explicit convergent subsequence rather than assuming convergence of the full penalty-minimizer sequence.
import Definitions.Def_VectorSpaceOpt_quadratic_penalty open Filter
namespace VectorSpaceOpt
/-- The feasibility half of Luenberger, Chapter 10, §10.11, Theorem 1. -/
theorem penalty_cluster_point_feasible
{X : Type*} [TopologicalSpace X] {p : ℕ} (G : X → Fin p → ℝ)
(x : ℕ → X) (x₀ : X) (phi : ℕ → ℕ)
(hphi : StrictMono phi)
(hxlim : Tendsto (x ∘ phi) atTop (nhds x₀))
(hvlsc : LowerSemicontinuousAt (constraintViolation G) x₀)
(hvlim : Tendsto (fun n => constraintViolation G (x n)) atTop (nhds 0)) :
IsConstraintFeasible G x₀ := by
sorry
end VectorSpaceOptRead-back
What the Lean code literally says, in plain math · gpt-5
Let be an arbitrary topological space, , , , , and . Assume is strictly increasing, the subsequence tends to along the natural-number at-top filter, the function is lower semicontinuous at , and the full real sequence tends to . Then for every . The topology on need not be Hausdorff and no continuity of itself is assumed. For , is identically zero and the feasibility conclusion is vacuous.
Confirmed by the mission captain (proposal self-audit).