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Feasibility of a penalty cluster point

Proved
VectorSpaceOpt.penalty_cluster_point_feasible

by wenxinzhang · Aug 25, 2026 · Mathlib c5ea003 (Lean v4.30.0)

cluster-pointfeasibilitylower-semicontinuitysubsequence

Let 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:

∀i,  Gi(x0)≤0.∀ i,\; G_i(x₀)≤0.∀i,Gi​(x0​)≤0.

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.

Preamble
import Definitions.Def_VectorSpaceOpt_quadratic_penalty

open Filter
Formal statement
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 VectorSpaceOpt
Source
David G. Luenberger, Optimization by Vector Space Methods (Wiley, 1969), Chapter 10, §10.11, feasibility part of Theorem 1, printed p. 306 (physical PDF p. 324). Scan: https://sites.science.oregonstate.edu/~show/old/142_Luenberger.pdf
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What the Lean code literally says, in plain math · gpt-5

Let XXX be an arbitrary topological space, p∈Np∈ℕp∈N, G:X→Fin(p)→RG:X→Fin(p)→ℝG:X→Fin(p)→R, x:N→Xx:ℕ→Xx:N→X, x0∈Xx₀∈Xx0​∈X, and φ:N→Nφ:ℕ→ℕφ:N→N. Assume φφφ is strictly increasing, the subsequence xφ(n)x_{φ(n)}xφ(n)​ tends to x0x₀x0​ along the natural-number at-top filter, the function V(y)=∑i:Fin(p)max(0,G(y)i)2V(y)=∑_{i:Fin(p)}max(0,G(y)_i)^2V(y)=∑i:Fin(p)​max(0,G(y)i​)2 is lower semicontinuous at x0x₀x0​, and the full real sequence V(xn)V(x_n)V(xn​) tends to 000. Then G(x0)i≤0G(x₀)_i≤0G(x0​)i​≤0 for every i:Fin(p)i:Fin(p)i:Fin(p). The topology on XXX need not be Hausdorff and no continuity of GGG itself is assumed. For p=0p=0p=0, VVV is identically zero and the feasibility conclusion is vacuous.

Human review
  • Endorsed by Shuze Chen · Aug 26, 2026

  • Endorsed by wenxinzhang · Aug 26, 2026

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

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