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Lemma 1, Case 2 — a Fejér-monotone sequence converges or its limit points lie on a sphere with axis LrL_rLr​

Proved
RelaxationMethod.LowDim.lemma1_case2

by mikedeng1 · Sep 27, 2026 · Mathlib 0df444a (Lean v4.33.1)

convergencefejer-monotonep2o-batch-p100bp2o-gran-per-chapterp2o-plan-paperp2o-v1sphere

Let A⊆EnA \subseteq E_nA⊆En​ be nonempty, let LrL_rLr​ be the affine span of AAA, and assume Lr≠EnL_r \neq E_nLr​=En​ (that is, r<nr < nr<n). Let {qν}\{q_\nu\}{qν​} be Fejér-monotone with respect to AAA. Then either {qν}\{q_\nu\}{qν​} converges to a point, or there is a point c∉Lrc \notin L_rc∈/Lr​ such that every limit point of {qν}\{q_\nu\}{qν​} lies on the spherical surface having LrL_rLr​ as its axis and passing through ccc:

every limit point x satisfies ∣x−a∣=∣c−a∣ for all a∈Lr.\text{every limit point } x \text{ satisfies } |x - a| = |c - a| \text{ for all } a \in L_r.every limit point x satisfies ∣x−a∣=∣c−a∣ for all a∈Lr​.

Together with Case 1 (r=nr = nr=n: convergence), this is the paper's Lemma 1. Case 2 is what makes a non-convergent run accumulate on a sphere around the axis LrL_rLr​, the starting point of both cases of Theorem 2.

Formalization Note The paper states the lemma for the polytope AAA; it is stated here for an arbitrary nonempty set AAA, which is stronger (the proof on p. 398 uses only that AAA spans LrL_rLr​, and §10 applies it to a convex set). "Limit point of the sequence" is a cluster point (MapClusterPt x atTop q), not a point of the closure of the range. The condition c∉Lrc \notin L_rc∈/Lr​ excludes the degenerate one-point locus.

Preamble
import Mathlib
import Definitions.Def_RelaxationMethod_Shared_FejerMonotone
import Definitions.Def_RelaxationMethod_LowDim_AxisSphere

open Filter Topology
Formal statement
namespace RelaxationMethod.LowDim

/-- Lemma 1, Case 2, p. 397: if `A` is nonempty and its affine span `L_r` is not the whole space
(`r < n`), a Fejér-monotone sequence with respect to `A` either converges or all its limit
points lie on one spherical surface having `L_r` as its axis. Stated for an arbitrary nonempty
set `A` (the paper states it for the polytope `A`). -/
theorem lemma1_case2 {n : ℕ} (A : Set (EuclideanSpace ℝ (Fin n))) (hA : A.Nonempty)
    (hr : affineSpan ℝ A ≠ ⊤) (q : ℕ → EuclideanSpace ℝ (Fin n))
    (hq : RelaxationMethod.Shared.IsFejerMonotone A q) :
    (∃ l, Tendsto q atTop (𝓝 l)) ∨
      ∃ c ∉ affineSpan ℝ A,
        ∀ x, MapClusterPt x atTop q → x ∈ axisSphere (affineSpan ℝ A) c := by sorry

end RelaxationMethod.LowDim
Source
Motzkin and Schoenberg, The relaxation method for linear inequalities, Canad. J. Math. 6 (1954), DOI 10.4153/CJM-1954-038-x, p. 397, §4, Lemma 1, Case 2 (proof p. 398)
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What the Lean code literally says, in plain math · claude-opus-5-5

Setting. Work in Rn\mathbb{R}^nRn with Euclidean distance.

  • A⊆RnA\subseteq\mathbb{R}^nA⊆Rn is an arbitrary nonempty set. It need not be a polytope, convex, or closed.
  • aff⁡(A)\operatorname{aff}(A)aff(A), the affine span of AAA, is assumed not to be all of Rn\mathbb{R}^nRn.
  • (qν)ν≥0(q_\nu)_{\nu\ge 0}(qν​)ν≥0​ is a sequence satisfying the predicate "Fejér-monotone with respect to AAA". That predicate comes from the imported definition RelaxationMethod.Shared.IsFejerMonotone, whose text is not included in the code provided, so the hypothesis cannot be unfolded here.
  • For an affine subspace LLL and a point ccc, write
S(L,c)={x:∣x−a∣=∣c−a∣ ∀a∈L}.S(L,c)=\{x: |x-a|=|c-a|\ \forall a\in L\}.S(L,c)={x:∣x−a∣=∣c−a∣ ∀a∈L}.

Claim. At least one of the following holds.

  1. (qν)(q_\nu)(qν​) converges to some point l∈Rnl\in\mathbb{R}^nl∈Rn.
  2. There is a point c∉aff⁡(A)c\notin\operatorname{aff}(A)c∈/aff(A) such that every cluster point of (qν)(q_\nu)(qν​) lies in S(aff⁡(A),c)S(\operatorname{aff}(A),c)S(aff(A),c). A cluster point is a point xxx such that every neighbourhood of xxx contains qνq_\nuqν​ for infinitely many ν\nuν. Membership in S(aff⁡(A),c)S(\operatorname{aff}(A),c)S(aff(A),c) means
∣x−a∣=∣c−a∣for all a∈aff⁡(A).|x-a|=|c-a| \quad\text{for all } a\in\operatorname{aff}(A).∣x−a∣=∣c−a∣for all a∈aff(A).

The point ccc is not required to be related to the sequence in any other way.

Degenerate cases:

  • No cluster points. If (qν)(q_\nu)(qν​) has no cluster points (for example, if ∣qν∣→∞|q_\nu|\to\infty∣qν​∣→∞), the second alternative holds automatically. A point c∉aff⁡(A)c\notin\operatorname{aff}(A)c∈/aff(A) always exists because aff⁡(A)≠Rn\operatorname{aff}(A)\ne\mathbb{R}^naff(A)=Rn.
  • One cluster point. If there is exactly one cluster point xxx, the second alternative holds with c=xc=xc=x whenever x∉aff⁡(A)x\notin\operatorname{aff}(A)x∈/aff(A).
  • n=0n=0n=0. The affine span of a nonempty set is the whole space, so the hypothesis on aff⁡(A)\operatorname{aff}(A)aff(A) fails and the statement is vacuous.
Human review
  • Endorsed by Shuze Chen · Sep 27, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Sep 27, 2026

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

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