Reflection positivity after positive normalization
ProvedFiniteSplitGibbsMethodsII.normalizedReflectionPositivityLet , , and be finite, and let have . If , then for every family , the reflected kernel constructed from is a symmetric positive-semidefinite real matrix. This theorem uses coefficient nonnegativity and positivity of the scalar normalizer; it neither assumes nor concludes pointwise nonnegativity or probability normalization.
import Definitions.Def_FiniteSplitGibbsMethodsII import Theorems.Thm_FiniteReflectionPositivityMethodsI_splitWeightReflectionPositivity open FiniteSplitGibbsMethodsII
theorem FiniteSplitGibbsMethodsII.normalizedReflectionPositivity :
NormalizedReflectionPositivityGate := by sorryRead-back
What the Lean code literally says, in plain math · gpt-5
For every universe-level-0 triple of types , each equipped with a finite enumeration, and every split-weight datum on indexed by , if for every and the split partition function , namely the finite sum of over all full configurations of , is strictly positive, then for every observable family , the -by- reflected kernel formed from the normalized weight and is positive semidefinite. The theorem assumes no pointwise nonnegativity of . All three types are permitted to be empty; whenever the strict-positivity hypothesis on is impossible the implication is vacuous, while an empty leaves the positive-semidefinite assertion with only its empty-index meaning.
Confirmed by the mission captain (proposal self-audit).