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Angle between column types, row types and a global signed type on a punctured rectangle

Proved
OAI.SignedSweeps.occupied_block_type_angle_exp_all

by wurtle · Nov 3, 2026 · Mathlib 0df444a (Lean v4.33.1)

representation-theorysigned-sweepsthorp-shuffle

This is the angle estimate between prescribed column types, prescribed row types and a prescribed global signed type on a rectangle with marked cells removed.

Throughout, SpS_pSp​ acts on the word space Wp=ℓ2((C⊔C)p)W_p=\ell^2\big((C\sqcup C)^p\big)Wp​=ℓ2((C⊔C)p), with C={1,…,q}C=\{1,\dots,q\}C={1,…,q} two-coloured into even and odd letters, by the signed action (ρ(g)f)(w)=ε(g,w) f(w∘g)(\rho(g)f)(w)=\varepsilon(g,w)\,f(w\circ g)(ρ(g)f)(w)=ε(g,w)f(w∘g), where ε(g,w)\varepsilon(g,w)ε(g,w) is the sign of the permutation ggg induces between the odd positions of www. For u+v=pu+v=pu+v=p and partitions α⊢u\alpha\vdash uα⊢u, β⊢v\beta\vdash vβ⊢v, the operator Pα,βP_{\alpha,\beta}Pα,β​ is the sum, over the placements of uuu even sites among the ppp sites, of the tensor product of the orthogonal projections onto the SαS^\alphaSα-isotypic part of the uuu even sites and the SβS^\betaSβ-isotypic part of the vvv odd sites. Its entropy is F(α,β)=∑a∈α∪βalog⁡u+vaF(\alpha,\beta)=\sum_{a\in\alpha\cup\beta}a\log\frac{u+v}{a}F(α,β)=∑a∈α∪β​alogau+v​, and a local type on kkk sites is such a pair of total size kkk. For an enumeration eee of the ppp sites by blocks j∈Jj\in Jj∈J with kjk_jkj​ sites each and local types t=(tj)jt=(t_j)_jt=(tj​)j​, PteP^{e}_{t}Pte​ is the corresponding tensor product of local type projections, with F(t)=∑jF(tj)F(t)=\sum_jF(t_j)F(t)=∑j​F(tj​). For a coefficient function f:G→Cf:G\to\mathbb{C}f:G→C on a finite group and a representation π\piπ, π(f)=∑gf(g)π(g)\pi(f)=\sum_gf(g)\pi(g)π(f)=∑g​f(g)π(g).

Let A,BA,BA,B be finite with BBB nonempty, let e:{1,…,n}→A×Be:\{1,\dots,n\}\to A\times Be:{1,…,n}→A×B be a bijection, and let p+l=np+l=np+l=n. Fix an injection zzz of lll marks into the nnn positions, and let x↦ι(x)x\mapsto\iota(x)x↦ι(x) enumerate the remaining ppp positions. Let eHe_HeH​ enumerate these ppp sites by column, with kjH≤sk^H_j\le skjH​≤s sites in column j∈Bj\in Bj∈B, and let eKe_KeK​ enumerate them by row, with kiK≤mk^K_i\le mkiK​≤m sites in row i∈Ai\in Ai∈A, compatibly with eee and ι\iotaι. Let H=(Hj)H=(H_j)H=(Hj​) and K=(Ki)K=(K_i)K=(Ki​) be local types on these blocks. Let u+v=pu+v=pu+v=p with α⊢u\alpha\vdash uα⊢u, β⊢v\beta\vdash vβ⊢v having at most q≥1q\ge1q≥1 rows, and let R≥max⁡(s,m)+1R\ge\max(s,m)+1R≥max(s,m)+1. Then

∥PHeH PKeK Pα,β∥2≤min⁡{1, exp⁡(F(H)+F(K)−F(α,β)+l+(∣A∣+∣B∣) 2q2log⁡R)}.\big\|P^{e_H}_{H}\,P^{e_K}_{K}\,P_{\alpha,\beta}\big\|^2\le\min\Big\{1,\ \exp\Big(F(H)+F(K)-F(\alpha,\beta)+l+(|A|+|B|)\,2q^2\log R\Big)\Big\}.​PHeH​​PKeK​​Pα,β​​2≤min{1, exp(F(H)+F(K)−F(α,β)+l+(∣A∣+∣B∣)2q2logR)}.

The estimate converts the entropies of line types into the global entropy; the removed cells cost only the factor ele^lel, with no assumption on their arrangement.

Formalization Note The norm is the operator norm on WpW_pWp​. The upstream statement is reproduced verbatim from BlockAngle.lean.

Preamble
import Mathlib
import Definitions.Def_SignedSweepMoment
import Definitions.Def_OAISignedSweepsProofDefs

open scoped BigOperators Classical
Formal statement
theorem OAI.SignedSweeps.occupied_block_type_angle_exp_all {A B : Type} [Fintype A] [Fintype B] [Nonempty B]
    {q p l n s m R : ℕ} (hq : 0 < q)
    (hn : p+l=n) (e : Fin n ≃ A × B) (z : MarkedAssignment l n)
    {kH : B → ℕ} {kK : A → ℕ}
    (eH : (Σ j, Fin (kH j)) ≃ Fin p)
    (hHroute : ∀ x, (eH.symm x).1 = (e (markedInjection hn z x)).2)
    (eK : (Σ i, Fin (kK i)) ≃ Fin p)
    (hKroute : ∀ x, (eK.symm x).1 = (e (markedInjection hn z x)).1)
    (H : ∀ j, PairType (kH j)) (K : ∀ i, PairType (kK i))
    (hkH : ∀ j, kH j ≤ s) (hkK : ∀ i, kK i ≤ m)
    (hR : max s m+1 ≤ R)
    {u v : ℕ} (h : u+v=p) (a : Partition u) (b : Partition v)
    (ha : a.1.colLen 0 ≤ q) (hb : b.1.colLen 0 ≤ q) :
    ‖(blockTypeProjection (C:=Fin q) eH H * blockTypeProjection eK K *
      pairTypeProjection h a b (Fin q)).toContinuousLinearMap‖^2 ≤
      min 1 (Real.exp (blockEntropy H+blockEntropy K-signedEntropy a b+l+
        ((Fintype.card A : ℝ)+Fintype.card B)*(2*(q:ℝ)^2*Real.log R))) := by sorry
Source
OpenAI, "Signed tensor densities and diagram budgets for the Thorp shuffle" (September 26, 2026), openai/math preprints/Signed-tensor-densities-and-diagram-budgets-for-coordinate-sweeps-September-26-2026/paper.pdf, Section 3, Theorem 3.2, and Section 4.6, eq. (25). Lean: https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/lean/OAI/Probability/SignedSweeps/BlockAngle.lean#L54-L84

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