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Finite split-Gibbs normalization and closure boundary

Proved
FiniteSplitGibbsMethodsII.claimBoundary

by lisamegawatts · 1 vote · Sep 19, 2026 · Mathlib c5ea003 (Lean v4.30.0)

counterexamplefinite-probabilitygibbs-weightsnormalizationpositive-semidefinitereflection-positivitysplit-weights

All seven component results hold simultaneously: finite normalization; half-factor and product closure with separate positivity clauses; reflection positivity after positive normalization; the combined probability, positive-semidefinite-kernel, and two-by-two bound; a pointwise-positive weight that normalizes to a probability weight while its raw matrix and raw and normalized delta kernels fail positive semidefiniteness; and zero split data with nonnegative coefficients and raw weight and positive-semidefinite raw kernels but zero partition function and no normalized probability weight. This conjunction packages the reusable positive results with both boundary controls, without identifying coefficient nonnegativity with pointwise weight positivity.

Preamble
import Theorems.Thm_FiniteSplitGibbsMethodsII_partitionNormalization
import Theorems.Thm_FiniteSplitGibbsMethodsII_halfFactorClosure
import Theorems.Thm_FiniteSplitGibbsMethodsII_productClosure
import Theorems.Thm_FiniteSplitGibbsMethodsII_normalizedReflectionPositivity
import Theorems.Thm_FiniteSplitGibbsMethodsII_normalizedSplitGibbs
import Theorems.Thm_FiniteSplitGibbsMethodsII_pointwisePositiveNoGo
import Theorems.Thm_FiniteSplitGibbsMethodsII_zeroWeightNoGo

open FiniteSplitGibbsMethodsII
Formal statement
theorem FiniteSplitGibbsMethodsII.claimBoundary : ClaimBoundary := by sorry
Source
Capstone conjunction authored for this sequel to Prove2Me mission db962332-7a71-4d34-a01c-e85cdee243ce. It packages exactly the seven local finite normalization, closure, RP, and counterfixture gates, while inheriting split-RP theorem 25912f71-137c-4b0d-b43b-3e25dcbd7163 and chessboard theorem 2350f552-ee7a-4011-8ac9-3dbaf1536361. Exact local gate: ClaimBoundary.
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What the Lean code literally says, in plain math · gpt-5

Using the following notation throughout: a split datum DDD indexed by AAA has coefficients cac_aca​, features fa:X→Rf_a:X\to\mathbb Rfa​:X→R, and associated weight wD(x,y)=∑a∈Acafa(x)fa(y)w_D(x,y)=\sum_{a\in A}c_af_a(x)f_a(y)wD​(x,y)=∑a∈A​ca​fa​(x)fa​(y); ZD=∑(x,y)∈X×XwD(x,y)Z_D=\sum_{(x,y)\in X\times X}w_D(x,y)ZD​=∑(x,y)∈X×X​wD​(x,y); the normalized weight is wD/ZDw_D/Z_DwD​/ZD​; the reflected kernel of a weight www and observables OiO_iOi​ has entries Kij=∑x,y∈XOi(x)w(x,y)Oj(y)K_{ij}=\sum_{x,y\in X}O_i(x)w(x,y)O_j(y)Kij​=∑x,y∈X​Oi​(x)w(x,y)Oj​(y); and positive semidefinite means ∑i,jqiKijqj≥0\sum_{i,j}q_iK_{ij}q_j\ge0∑i,j​qi​Kij​qj​≥0 for every real family qqq. The theorem asserts the conjunction of exactly seven gates: (1) for every finite type Ω\OmegaΩ and weight w:Ω→Rw:\Omega\to\mathbb Rw:Ω→R, pointwise nonnegativity of www followed by existence of some ω\omegaω with w(ω)>0w(\omega)>0w(ω)>0 implies both ∑ωw(ω)>0\sum_{\omega}w(\omega)>0∑ω​w(ω)>0 and that w(ω)/∑ηw(η)w(\omega)/\sum_\eta w(\eta)w(ω)/∑η​w(η) is pointwise nonnegative with total sum exactly 111; (2) for every type XXX, finite type AAA, function h:X→Rh:X\to\mathbb Rh:X→R, and split datum DDD, replacing each feature fa(x)f_a(x)fa​(x) by h(x)fa(x)h(x)f_a(x)h(x)fa​(x) while leaving cac_aca​ unchanged gives weight h(x)h(y)wD(x,y)h(x)h(y)w_D(x,y)h(x)h(y)wD​(x,y) for every x,yx,yx,y, preserves coefficient nonnegativity whenever all original coefficients are nonnegative, and gives a pointwise nonnegative new weight whenever hhh and the original weight are both pointwise nonnegative; (3) for every type XXX, finite types A,BA,BA,B, and split data D,ED,ED,E, the product-indexed datum with coefficient cadbc_ad_bca​db​ and feature fa(x)gb(x)f_a(x)g_b(x)fa​(x)gb​(x) has weight wD(x,y)wE(x,y)w_D(x,y)w_E(x,y)wD​(x,y)wE​(x,y), has nonnegative coefficients whenever both inputs do, and has pointwise nonnegative weight whenever both input weights do; (4) for every finite types X,A,IX,A,IX,A,I and split datum DDD, nonnegative coefficients and ZD>0Z_D>0ZD​>0 imply, for every observable family, that the reflected kernel of wD/ZDw_D/Z_DwD​/ZD​ is positive semidefinite; (5) for every finite types X,A,IX,A,IX,A,I and split datum DDD, nonnegative coefficients, pointwise nonnegative weight, and existence of x,yx,yx,y with wD(x,y)>0w_D(x,y)>0wD​(x,y)>0 imply, for every observable family, that wD/ZDw_D/Z_DwD​/ZD​ on X×XX\times XX×X is a probability weight, its reflected kernel KKK is positive semidefinite, and Kij2≤KiiKjjK_{ij}^2\le K_{ii}K_{jj}Kij2​≤Kii​Kjj​ for every i,ji,ji,j; (6) on Fin⁡(2)\operatorname{Fin}(2)Fin(2), the weight Wij=1W_{ij}=1Wij​=1 for i=ji=ji=j and 222 otherwise is asserted to be pointwise nonnegative and symmetric, to have positive partition sum 666, and after division by 666 to be a probability weight, while WWW, its reflected kernel against the delta observables, and the corresponding reflected kernel after normalization are each not positive semidefinite; and (7) for the one-index split datum with coefficient 000 and feature constantly 111, coefficients and weights are nonnegative for every type XXX, the partition sum is 000 for every finite XXX, every reflected kernel is positive semidefinite for all finite X,IX,IX,I and all observables, and the normalized pair-weight is not a probability weight for every finite XXX. All quantified finite types may be empty: positive-existence or positive-partition hypotheses make the relevant implications vacuous when they cannot be met; universal claims over empty point or coefficient types can be vacuous; kernels indexed by empty III are zero-dimensional; and in gate (7), real division by the zero partition is total and gives the identically zero normalized weight, whose total mass is 000, not 111, even for empty XXX.

Human review
  • Endorsed by Shuze Chen · Sep 23, 2026

  • Endorsed by lisamegawatts · Sep 23, 2026

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

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