Alphabeta Math
DefinitionDefinition: Literature-sourcedProof: Not applicablePipeline-generatedprecheck passverified 2026-10-22 (gpt-6.1-sol)
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Finite FK random-cluster weights for real q and boundary partitions

Definition

Let G=(V,E,∂) be a finite undirected multigraph: V and the edge-label set E are finite, and each e∈E has a singleton or two-element endpoint set ∂e⊆V. Distinct labels may have the same endpoint set. Let B⊆V be a designated boundary set and let ξ be an equivalence relation on B; vertices in each ξ-class are wired together, while vertices outside B remain separate. For A⊆E, let kξ(A) be the number of connected components, including isolated vertices, after wiring the ξ-classes and opening exactly the edges in A. A loop does not join distinct vertices.

For q∈[1,∞) and p∈(0,1), the finite FK weight of A and its partition function are

wG,p,qξ(A):=p∣A∣(1−p)∣E∖A∣qkξ(A),ZG,p,qξ:=∑A⊆EwG,p,qξ(A).

The finite FK random-cluster probability is ϕG,p,qξ(A):=wG,p,qξ(A)/ZG,p,qξ. Every weight is positive and finite, and the finite sum ZG,p,qξ is positive and finite. The free boundary condition is the identity partition on B; the wired boundary condition identifies all vertices of B in one class.

When G is a finite simple graph with the Potts/FK convention of the earlier finite Potts definition, ξ is free, q=Q is an integer at least two, and every activity equals v=p/(1−p), this definition gives the same normalized FK law, since wFK(A)=(1−p)−∣E∣wG,p,Qξ(A).

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