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Loop removal in the weighted Kuramoto calculus

Statement

Let A be a network on a nonempty finite set V, with W=A−diag⁡(Axx), row degrees dA(x) and dW(x), and Laplacians LA and LW, as in Finite symmetric networks and loop removal. For phases θ∈RV define EA(θ)=12∑x,yAxy(1−cos⁡(θx−θy)) and FA(θ)x=−∑yAxysin⁡(θx−θy). Then EA=EW, FA=FW=−∇EA, and LA=LW. Moreover fT∇2EA(θ)f=12∑x,yAxycos⁡(θx−θy)(fx−fy)2 for every real f. Thus arbitrary diagonal weights affect row degrees but none of these energy or dynamical expressions.

Facts & Assumptions

[F1]

The network degree and Laplacian are row sum and degree matrix minus adjacency; removing the diagonal leaves the Laplacian unchanged (Finite symmetric networks and loop removal).

[F2]

The gradient and Hessian of a symmetric periodic pair potential are given by the finite pair-potential formulas (Differentiation of symmetric periodic pair potentials).

[F3]

For real angles, z(t)=cos⁡t+isin⁡t has modulus one and satisfies z(u−v)=z(u)z(v)‾. Also sin⁡′(t)=cos⁡t and cos⁡′(t)=−sin⁡t; these identities and derivatives are 2π-periodic (Phasors, trigonometric identities and periodic derivatives).

Proof

Given: The data in the Statement.

Goal: Establish the stated conclusions.

1.1F1F3algebra

Taking u=v in [F3] gives z(0)=∣z(u)∣2=1, hence cos⁡0=1 and sin⁡0=0. For x=y, both 1−cos⁡(θx−θy) and sin⁡(θx−θy) vanish. Deleting exactly those summands therefore leaves EA and FA unchanged. Also dA(x)=dW(x)+Axx, so the identical diagonal correction in the degree matrix and in A cancels in the Laplacian, giving LA=LW.

2.1F2F3step 1.1algebra∎

Apply [F2] with v(t)=1−cos⁡t, whose derivatives are v′(t)=sin⁡t and v′′(t)=cos⁡t. By [F3] and step 1.1, z(−t)=z(0)z(t)‾=z(t)‾, so equality of real parts gives cos⁡(−t)=cos⁡t. Thus v is even. The derivative identities in [F3] make v twice continuously differentiable, and its periodicity follows from [F3]. The resulting gradient is −FA, and its Hessian quadratic form is the displayed formula. Every diagonal Hessian term vanishes because fx−fx=0. This proves all assertions.

Depends on

Used by · 2

Dependency tree · 2 levels

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