Induced action and interactions [fdip-005Q]
✍️sourceAGENTDRAFTED
- November 1, 2026
Induced action and interactions [fdip-005Q]
✍️sourceAGENTDRAFTED
- November 1, 2026
Integrating all fields produces a scalar that depends on the supplied graph weights and mass. Its derivatives measure fluctuations of edge differences; its determinant also counts rooted forests. These are two descriptions of the same finite Gaussian normalization. The selected-background comparison distinguishes changes of the supplied data from coordinate changes. In this full-field integral the selector plays no role.
The four-cycle makes the derivative and forest formulas explicit. The circle example then keeps the finite determinant and its chosen normalization visible while taking a mesh limit. No continuum measure or canonical infinite-dimensional determinant is needed for that limit.
Keeping a selected field and integrating its complement instead produces an interaction on the retained space. Conditional means and covariances determine its formal coefficients. Their transport follows the same background arrow, provided it also transports the supplied interaction. A quartic cycle example shows how graph weights and the selector enter the induced vertices, without asserting convergence of the perturbation series.
Definition 1. The Gaussian induced action [fdip-005R]AGENTDRAFTED
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Definition 1. The Gaussian induced action [fdip-005R]AGENTDRAFTED
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Fix a finite nonempty vertex set \(V\), with \(n=|V|\), and a finite set \(E\) of labelled edges with source and head maps. Orientations are auxiliary. In the counting metric on \(\mathbb R^V\), put \(d_e=\delta _{h(e)}-\delta _{s(e)}\). For weights \(w_e\ge 0\) and mass \(m>0\), write \[L_w=\sum _{e\in E}w_e d_ed_e^T,\qquad Q=L_w+m^2I,\qquad G=Q^{-1}.\] A zero weight means the corresponding edge is deleted. Parallel labels remain distinct, and a loop has \(d_e=0\). Thus the boundary faces of this weight orthant describe subgraphs, extending the positive-weight background convention.
The Gaussian induced action, relative to counting-metric Lebesgue measure, is \[\mathcal W(w,m)=\frac 12\log \det Q.\] The Gaussian normalizer gives \[Z(w,m)=\int _{\mathbb R^V}e^{-\phi ^TQ\phi /2}\,d\phi =(2\pi )^{n/2}(\det Q)^{-1/2},\] so \(-\log Z=\mathcal W-(n/2)\log (2\pi )\). This scalar is invariant under graph relabeling and orthogonal conjugation of \(Q\). It does not by itself retain all graph or selector data.
For fixed \(m>0\), differentiation in the weights means differentiation of the smooth extension to \[\mathcal U_m=\{w\in \mathbb R^E:L_w+m^2I>0\}.\] This open convex set contains the nonnegative orthant. For \(w_e\ge 0\) and \(x\ne 0\), indeed, \[x^TQx=\sum _e w_e(d_e^Tx)^2+m^2\|x\|^2>0.\] In particular the formulas below remain meaningful at deleted edges, even though only nonnegative weights are interpreted as graph conductances.
Theorem 2. An edge derivative is half its variance [fdip-005S]AGENTDRAFTED
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Theorem 2. An edge derivative is half its variance [fdip-005S]AGENTDRAFTED
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For the induced action, with \(m>0\) fixed, \[\frac {\partial \mathcal W}{\partial w_e} =\frac 12 d_e^TGd_e =\frac 12\mathbb E[(\phi _{h(e)}-\phi _{s(e)})^2],\] where \(\phi \sim N(0,G)\). Each derivative is nonnegative.
Proof.
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Proof.
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For any square matrix \(B\), multilinearity of the determinant gives \(\det (I+tB)=1+t\operatorname {tr}B+O(t^2)\): the linear terms select one diagonal entry of \(tB\), and all higher terms use at least two entries of \(tB\). Factoring an invertible \(Q\) yields \[\det (Q+tA)=\det Q\det (I+tQ^{-1}A).\] When \(Q>0\) and \(A\) is symmetric, the perturbed matrix is positive for sufficiently small \(|t|\). Differentiating its real logarithm therefore gives \[\left .\frac {d}{dt}\log \det (Q+tA)\right |_{t=0} =\operatorname {tr}(Q^{-1}A).\] Set \(A=d_ed_e^T\); cyclicity of trace gives \(\operatorname {tr}(Gd_ed_e^T)=d_e^TGd_e\). Finally the covariance identity says \(\mathbb E[(d_e^T\phi )^2]=d_e^TGd_e\ge 0\).
The matrix differential is the log-determinant gradient in
[boyd2004convex, Appendix A.4.1, pp. 641-642].
TauCeti's Matrix.fderiv_det_apply states
\(D\det (Q)[A]=\det (Q)\operatorname {tr}(Q^{-1}A)\) for a finite
matrix over a complete nontrivially normed field whose determinant
is a unit. Over \(\mathbb R\), positivity supplies this hypothesis.
It supports the determinant derivative; the logarithm, edge direction
and Gaussian interpretation are the steps proved here.
Theorem 3. Concavity and covariance of squared edge differences [fdip-005T]AGENTDRAFTED
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Theorem 3. Concavity and covariance of squared edge differences [fdip-005T]AGENTDRAFTED
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On the open set \(\mathcal U_m\) of Definition 1, \[\frac {\partial ^2\mathcal W}{\partial w_e\partial w_f} =-\frac 12(d_e^TGd_f)^2.\] The induced action is concave, including on the nonnegative weight orthant. For the corresponding centered Gaussian field, \[\operatorname {Cov}((d_e^T\phi )^2,(d_f^T\phi )^2) =2(d_e^TGd_f)^2,\] so the Hessian is minus one quarter of this covariance matrix.
Proof.
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Proof.
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Differentiating \(QG=I\) in weight \(w_f\) gives \(\partial _fG=-Gd_fd_f^TG\). Substitute this into Theorem 2; symmetry of \(G\) gives the Hessian entry. For a real weight direction \(a=(a_e)\), set \(A=\sum _e a_e d_ed_e^T\). Its second directional derivative is \[D^2\mathcal W[a,a] =-\frac 12\operatorname {tr}(GAGA) =-\frac 12\operatorname {tr}((G^{1/2}AG^{1/2})^2)\le 0.\] The last matrix is real symmetric, and the trace of its square is the sum of the squares of its entries. Restricting to a line segment in the convex set \(\mathcal U_m\) proves concavity. This is also the Gram-square form of the Schur-product argument.
For completeness let \(X=d_e^T\phi \), \(Y=d_f^T\phi \), with variances \(\alpha ,\beta \) and covariance \(\gamma \). Their moment-generating function is \[\mathbb E[e^{sX+tY}] =\exp \left (\frac {\alpha s^2+2\gamma st+\beta t^2}{2}\right ).\] Differentiation at zero is justified by Gaussian exponential integrability, also when this pair is degenerate. The coefficient of \(s^2t^2\) is \(\alpha \beta /4+\gamma ^2/2\), hence \(\mathbb E[X^2Y^2]=\alpha \beta +2\gamma ^2\). Subtracting \(\mathbb E[X^2]\mathbb E[Y^2]\) proves the covariance identity.
[friedli2017statistical, Exercise 8.3, p. 385] states Wick's formula. The last calculation is its four-factor case and agrees with the Gaussian contraction rule. Concavity need not be strict in the weights: loops or dependent matrices \(d_ed_e^T\) can give zero second derivatives in nonzero directions.
Proposition 4. A massive sum rule and Foster's identity [fdip-005U]AGENTDRAFTED
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Proposition 4. A massive sum rule and Foster's identity [fdip-005U]AGENTDRAFTED
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For fixed nonnegative weights and \(m>0\), \[\sum _e w_e\frac {\partial \mathcal W}{\partial w_e} =\frac 12(n-m^2\operatorname {tr}G).\] If the positive-weight support is connected, its limit as \(m\downarrow 0\) is \((n-1)/2\). More explicitly, let \(L_w^+\) be zero on constant fields and the inverse of \(L_w\) on their orthogonal complement. The effective resistance between the endpoints of an edge is \(R_{\mathrm {eff}}(e)=d_e^TL_w^+d_e\), and \[\sum _e w_eR_{\mathrm {eff}}(e)=n-1.\] The resistance definition agrees with [spielman2009effective, p. 2].
Proof.
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Proof.
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The edge derivative and cyclicity of trace give \[\sum _e w_e\frac {\partial \mathcal W}{\partial w_e} =\frac 12\operatorname {tr}(GL_w) =\frac 12\operatorname {tr}(I-m^2G).\] For connected positive support, the energy expression for \(L_w\) shows that its kernel consists exactly of constants. Choose an orthonormal eigenbasis \(u_0=n^{-1/2}\mathbf 1,u_1,\ldots ,u_{n-1}\), with positive nonconstant eigenvalues \(\lambda _j\). Then \[G=m^{-2}u_0u_0^T+ \sum _{j=1}^{n-1}(\lambda _j+m^2)^{-1}u_ju_j^T.\] Thus \(m^2\operatorname {tr}G\to 1\). Every incidence vector \(d_e\) is perpendicular to constants, so its quadratic form does not contain the divergent first term and tends to \(d_e^TL_w^+d_e\). Passing to the limit in the finite sum proves Foster's identity.
This is the projection-trace sum in [spielman2009effective, Lemma 3(iii), pp. 6-7; proof of Theorem 1, p. 8]. The massive law itself has no limit as a probability law on the full field space: its constant-mode variance diverges. Only the displayed edge observables have been passed to the limit. With \(k\) connected components the same eigenbasis argument replaces \(1\) by \(k\) in the trace limit.
Theorem 5. The determinant as a rooted-forest sum [fdip-005V]AGENTDRAFTED
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Theorem 5. The determinant as a rooted-forest sum [fdip-005V]AGENTDRAFTED
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A rooted spanning forest on \(V\) is an acyclic set \(F\subseteq E\) together with exactly one chosen vertex in each component, including isolated vertices. Write \(R\) for these roots and \(r=|R|\). Loops cannot occur, and distinct parallel labels count as distinct choices. With the data of Definition 1, \[\det (L_w+m^2I) =\sum _{(F,R)}\left (\prod _{e\in F}w_e\right )m^{2|R|}.\] Normalize these nonnegative terms to obtain a probability law on rooted spanning forests. It satisfies \[\begin {aligned} \mathbb P(e\in F)&=w_ed_e^TGd_e,\\ \mathbb E[|R|]&=m^2\operatorname {tr}G,\\ \mathbb E[|F|]&=n-m^2\operatorname {tr}G. \end {aligned}\] If the positive-weight support is connected, with weighted spanning-tree sum \(\tau _w\), then \[\det (L_w+m^2I)=n\tau _wm^2+O(m^4) \qquad (m\downarrow 0).\] Thus the spanning-tree coefficient appears after division by \(m^2\); the unscaled determinant tends to zero.
Proof.
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Proof.
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Form the \(n\times (|E|+n)\) matrix with columns \(\sqrt {w_e}d_e\) for edges and \(m\delta _v\) for vertices. Call it \(A\); then \(AA^T=L_w+m^2I\). Cauchy-Binet expresses its determinant as the sum of the squared \(n\times n\) minors. A chosen minor consists of edge columns \(F\) and vertex columns \(R\), with \(|F|+|R|=n\). A cycle in \(F\) makes its incidence columns dependent (this includes a loop and the two-edge cycle of parallel edges), so only forests contribute.
Suppose \(F\) is a forest with components \(C_i\). Their incidence columns span the coordinate-sum-zero subspace in each \(\mathbb R^{C_i}\), of dimension \(|C_i|-1\). This follows by successively removing leaves. A component with no chosen vertex column therefore prevents full rank. There are as many chosen vertex columns as components, because \(|R|=n-|F|\); hence full rank requires exactly one in each component. Conversely, for a tree with one root, the matrix of its unweighted incidence columns and that root's coordinate vector has determinant \(\pm 1\): remove a nonroot leaf and expand along its row, then finish with the root's \(1\times 1\) matrix. Applying this to all components proves that precisely the stated forests contribute, with squared minor \((\prod _{e\in F}w_e)m^{2|R|}\). Zero-weight edge columns vanish, consistent with deletion.
Put \(t=m^2\) and call this polynomial \(D(w,t)\). Termwise differentiation gives \(w_e\partial _eD=\sum _{F\ni e,R}(\prod _{f\in F}w_f)t^{|R|}\) and \(t\partial _tD=\sum _{F,R}|R|(\prod _{f\in F}w_f)t^{|R|}\). Divide by \(D>0\) and use the determinant derivative from Theorem 2, first in direction \(d_ed_e^T\), then in direction \(I\). The edge identity holds also at \(w_e=0\), when both sides vanish. Finally every forest has \(|F|+|R|=n\). For connected positive support, the coefficient of \(t\) comes from spanning trees; each has \(n\) choices of root. All other terms have at least two roots and hence order \(t^2\).
Equivalently, add a new boundary vertex \(*\) with an edge of weight \(m^2\) to each old vertex. Attach \(*\) to the chosen root of each forest component; this is a bijection to spanning trees of the augmented graph. Its Dirichlet Laplacian at \(*\) is \(L_w+m^2I\). This is the trivial-connection case of Kenyon's boundary theorem: cycle factors vanish, leaving trees rooted at the boundary [kenyon2011spanning, Theorem 7, Section 8, p. 15]. The conductance extension is given in [kenyon2011spanning, Section 7.1, Theorem 5, p. 13]. The proof above includes the needed weights and zero-weight faces. Chaiken's all-minors matrix-tree theorem enumerates directed forests by the minors of a Laplacian whose columns sum to zero [chaiken1982minors, pp. 319-329]. The expansion proved above is the undirected weighted case, obtained from Cauchy-Binet. It does not reproduce Chaiken's directed sign rule.
TauCeti's
Matrix.det_mul_transpose_eq_sum_plucker_sq
gives the Cauchy-Binet identity for a matrix over a commutative
ring with rows indexed by \(\mathrm {Fin}(n)\) and a finite ordered
column set. Reindexing the present real matrix supplies those
hypotheses. Its Plücker coordinates are the maximal minors.
This supports the sum-of-squares step; the classification of
nonzero forest minors and the probability consequences are
proved here.
Example 6. Edge derivatives and forests on an alternating cycle [fdip-005W]AGENTDRAFTED
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Example 6. Edge derivatives and forests on an alternating cycle [fdip-005W]AGENTDRAFTED
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Take the four-cycle \(0\to 1\to 2\to 3\to 0\) with weights \((1,2,1,2)\) and \(m=1\). The orthonormal vectors \[\begin {aligned} u_0&=\tfrac 12(1,1,1,1),&u_2&=\tfrac 12(1,-1,-1,1),\\ u_4&=\tfrac 12(1,1,-1,-1),&u_6&=\tfrac 12(1,-1,1,-1) \end {aligned}\] have \(L_w\)-eigenvalues given by their subscripts, as direct multiplication verifies. Therefore \(Q\) has eigenvalues \(1,3,5,7\), and \[\det Q=105,\qquad \operatorname {tr}G =1+\frac 13+\frac 15+\frac 17=\frac {176}{105}.\] The edge vector \(d_{01}\) has squared projection \(1\) onto each of \(u_2,u_6\), and zero onto the other two. For \(d_{12}\) the nonzero squared projections are onto \(u_4,u_6\). Half-turn symmetry gives the other two edges. Thus \[\frac {\partial \mathcal W}{\partial w_e} =\begin {cases} \frac 12(\frac 13+\frac 17)=\frac 5{21}&w_e=1,\\ \frac 12(\frac 15+\frac 17)=\frac 6{35}&w_e=2. \end {cases}\] These are individual edge derivatives at the displayed weights; they are not derivatives of a simultaneous change of both edges of one weight. The weighted sum is \[2\cdot \frac 5{21}+4\cdot \frac 6{35} =\frac {122}{105} =\frac 12\left (4-\frac {176}{105}\right ).\] This is an instance of Proposition 4.
The forest generating polynomial is \[\begin {aligned} \det (tI+L_w)&=t(t+2)(t+4)(t+6)\\ &=t^4+12t^3+44t^2+48t. \end {aligned}\] The 4 spanning trees have weights \(4,2,4,2\), so \(\tau _w=12\) and the coefficient of \(t\) is \(4\tau _w=48\). At \(t=1\), the forest law has \[\mathbb E[|R|]=\frac {176}{105},\qquad \mathbb E[|F|]=\frac {244}{105}.\] Its individual edge probabilities are \(10/21\) on weight-one edges and \(24/35\) on weight-two edges. They sum to \(244/105\), as the forest identity requires.
On the unit four-cycle, all 4 edges instead have the same effective resistance by rotation. One unit edge is in parallel with the other three in series, giving \(R_{\mathrm {eff}}=3/4\). Equivalently the nonconstant spectral calculation gives \(1/2+1/4=3/4\). Foster's sum is \(4(3/4)=3\), matching \(n-1\). This is an edge-observable limit, not a massless Gaussian law on all 4 vertex values.
Example 7. A finite cycle determinant and its circle scaling [fdip-005X]AGENTDRAFTED
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Example 7. A finite cycle determinant and its circle scaling [fdip-005X]AGENTDRAFTED
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For the unit-weight cycle \(C_n\), \(n\ge 3\), let \(c>0\) and define \(\omega >0\) by \(2\cosh \omega =2+c\). Then \[\det (cI+L_{C_n})=2\cosh (n\omega )-2 =4\sinh ^2(n\omega /2).\] This uses the nearest-neighbor Laplacian \(L_{C_n}=2I-T_n-T_n^{-1}\), with every undirected edge counted once. The condition \(n\ge 3\) avoids replacing the two-vertex cycle by a different parallel-edge convention.
Proof.
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Proof.
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Complex Fourier vectors diagonalize \(T_n\); write \(\zeta _k=e^{2\pi ik/n}\) and \(z=e^\omega >1\). The eigenvalues of \(cI+L_{C_n}\) are \[c+2-\zeta _k-\zeta _k^{-1} =z^{-1}(z-\zeta _k)(z-\zeta _k^{-1}).\] Since inversion permutes the \(n\)-th roots of unity, their product is \[z^{-n}(z^n-1)^2=z^n+z^{-n}-2.\] This equals the real determinant: complexification does not change the determinant of the original real matrix.
Now fix \(\mu >0\), set \(c=\mu /n^2\), and write \(\omega _n\) for the corresponding solution. Since \(2\cosh x-2=x^2+O(x^4)\) at zero, \(\omega _n\to 0\) and \(n\omega _n\to \sqrt \mu \). Therefore \[\det (\mu n^{-2}I+L_{C_n}) \longrightarrow 4\sinh ^2(\sqrt {\mu }/2).\] For the circle mesh, with \(L_n=n^2L_{C_n}\) and \(Q_n=L_n+\mu I\), this is exactly \[\frac {\det Q_n}{n^{2n}} \longrightarrow 4\sinh ^2(\sqrt {\mu }/2),\] or, in the induced-action notation, \[\frac 12\log \det Q_n-n\log n \longrightarrow \log \left (2\sinh (\sqrt {\mu }/2)\right ).\] The determinant here is that of the operator \(Q_n\), equivalently its matrix in an orthonormal basis for the supplied normalized pairing. In raw vertex coordinates the action-form matrix is \(Q_n/n\); its determinant carries the additional factor \(n^{-n}\). Thus a reference measure convention still matters when passing from this operator calculation to a Gaussian integral.
This finite-product limit complements the fixed-mode covariance limit and the exact cycle covariance. It does not define an infinite product without a normalization, prove convergence of full-field measures, or identify the limit with a continuum determinant under an unstated regularization convention. The positive mass is fixed throughout.
Definition 8. An interaction on a selected background [fdip-005Y]AGENTDRAFTED
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Definition 8. An interaction on a selected background [fdip-005Y]AGENTDRAFTED
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An interacting selected background consists of a selected Gaussian background \(b=(\Pi ,w,m,P)\), a real polynomial \(\mathcal V:H\to \mathbb R\), and a formal variable \(\lambda \). Thus the precision \(Q>0\), metric and selector are supplied along with the interaction. Write \(X=\operatorname {im}P\) and \(Y=\ker P\), so \(H=X\mathbin \perp Y\).
For every \(x\in X\), define a probability law on \(Y\) by \[\begin {aligned} Z_b(x)&=\int _Y e^{-\frac 12\langle x+y,Q(x+y)\rangle }\,dy,\\ \kappa _{b,x}(dy) &=Z_b(x)^{-1}e^{-\frac 12\langle x+y,Q(x+y)\rangle }\,dy. \end {aligned}\] The volume \(dy\) is the induced Euclidean volume. Positive precision makes \(Z_b(x)\) positive and finite. The square-completion formula shows that this is a Gaussian law whose mean depends linearly on \(x\). It is the conditional law of the eliminated field given the retained value, using the canonical density formula at every \(x\); general regular conditional laws are unique only almost everywhere.
The fiber effective interaction is the formal series \[\begin {aligned} M_{b,\mathcal V}(\lambda ,x) &=\sum _{n\ge 0}\frac {(-\lambda )^n}{n!} \int _Y\mathcal V(x+y)^n\,\kappa _{b,x}(dy),\\ F_{b,\mathcal V}(\lambda ,x)&=-\log M_{b,\mathcal V}(\lambda ,x). \end {aligned}\] Each coefficient is a polynomial on \(X\): expand the polynomial about the conditional mean and use the fixed centered Gaussian moments. These moments are finite by polynomial integrability. The series \(M\) has constant coefficient one, so its logarithm is a formal power series in \(\lambda \) with coefficients in the real polynomial algebra \(\mathcal P(X)\) on \(X\). This extends the quartic construction to any polynomial interaction and any orthogonal selector. Constants in \(x\) remain part of \(F\).
This definition is coefficientwise. It imposes no positivity on \(\mathcal V\), asserts no convergence for nonzero \(\lambda \), and uses no interchange of an infinite sum with an integral. The notation \(O(\lambda ^k)\) means divisibility by \(\lambda ^k\). A fiber logarithm is different from the Legendre transform that defines a one-particle irreducible effective action in [etingof2024mathematical, v2, Proposition 3.23, p. 50]. Here the retained field is fixed while its orthogonal complement is integrated.
Theorem 9. Naturality of the fiber interaction [fdip-005Z]AGENTDRAFTED
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Theorem 9. Naturality of the fiber interaction [fdip-005Z]AGENTDRAFTED
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Let \(b,b'\) be selected backgrounds with polynomial interactions \(\mathcal V,\mathcal V'\). Suppose a background arrow has vertex counting isometry \(U:H\to H'\) and satisfies \(\mathcal V'\circ U=\mathcal V\). Then the fiber interactions obey, to all formal orders, \[F_{b',\mathcal V'}(\lambda ,Ux) =F_{b,\mathcal V}(\lambda ,x)\qquad (x\in X).\] The assertion also holds for signed endpoint arrows. More generally, it holds for an orthogonal isomorphism \(O:H\to H'\) satisfying \[Q'=OQO^{-1},\qquad P'=OPO^{-1},\qquad \mathcal V'\circ O=\mathcal V,\] with \(Ox\) in place of \(Ux\). This latter statement uses the wider orthogonal comparison, which need not preserve incidence.
Proof.
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Proof.
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It suffices to prove the orthogonal statement. The equation for \(P'\) gives isometries \(O_X:X\to X'\) and \(O_Y:Y\to Y'\). For \(x\in X\) and \(y\in Y\), \[\langle O_Xx+O_Yy,Q'(O_Xx+O_Yy)\rangle =\langle x+y,Q(x+y)\rangle .\] The change of variables \(y'=O_Yy\) has absolute Jacobian one for the induced Euclidean volumes. Consequently \(Z_{b'}(O_Xx)=Z_b(x)\) and \[(O_Y)_*\kappa _{b,x}=\kappa _{b',O_Xx}.\] Transport of \(\mathcal V\) now gives, for every integer \(n\ge 0\), \[\begin {aligned} &\int _{Y'}\mathcal V'(O_Xx+y')^n\,\kappa _{b',O_Xx}(dy')\\ &\qquad =\int _Y\mathcal V(x+y)^n\,\kappa _{b,x}(dy). \end {aligned}\] The integrals are finite. Thus the two moment series agree after pullback, and so do their formal logarithms. A directed or signed background arrow supplies the required conjugacy of \(Q,P\), proving the graph cases as well.
Each coefficient of \(F\) is therefore a polynomial transported by the same retained isometry. Its homogeneous parts correspond by polarization to symmetric tensors on \(X\). A background automorphism that also preserves \(\mathcal V\) fixes these polynomials and tensors; choosing coordinates only records their components. Arbitrary components need not be unchanged by an orthogonal change of basis. Likewise a graph symmetry need not preserve a supplied interaction. The invariance assertion uses both the background and interaction transport equations.
Proposition 10. Conditional propagators and induced vertices [fdip-0060]AGENTDRAFTED
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Proposition 10. Conditional propagators and induced vertices [fdip-0060]AGENTDRAFTED
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For an interacting selected background, put \(n=\dim H\) and \(r=\operatorname {rank}P\), and choose orthonormal basis maps \(B:\mathbb R^r\to X\) and \(B_\perp :\mathbb R^{n-r}\to Y\). Thus a full field is \(\phi =Bx+B_\perp y\); in this proposition \(x,y\) denote coordinates. Set \[\begin {aligned} A&=B^TQB,&K&=B^TQB_\perp ,\\ D&=B_\perp ^TQB_\perp >0,&S&=A-KD^{-1}K^T. \end {aligned}\] The eliminated coordinates, conditionally on \(x\), have law \[y=\mu (x)+\eta ,\qquad \mu (x)=-D^{-1}K^Tx,\qquad \eta \sim N(0,D^{-1}).\] In particular the full conditional mean and noise covariance are \[h(x)=Bx+B_\perp \mu (x),\qquad C=B_\perp D^{-1}B_\perp ^T.\] The map \(h\) is the harmonic extension: \(B^Th(x)=x\) and \(B_\perp ^TQh(x)=0\). It is the unique minimizer of the quadratic energy among fields with these retained coordinates.
Write \(F_\lambda (x)=F_{b,\mathcal V}(\lambda ,Bx)\). Then \[\begin {aligned} a(x)&=\mathbb E\mathcal V(h(x)+B_\perp \eta ),\\ b(x)&=\operatorname {Var}\bigl (\mathcal V(h(x)+B_\perp \eta )\bigr ),\\ F_\lambda (x)&=\lambda a(x)-\frac {\lambda ^2}{2}b(x) +O(\lambda ^3). \end {aligned}\] Thus external mean factors in the conditional moment expansion use \(h(x)\), and each internal pairing of eliminated coordinates uses \(D^{-1}\). These are the background-dependent Feynman rules for this fiber integral. In full-field components the internal pairing is \(C\); the eliminated mean \(\mu \) alone omits the retained part of \(h\). For a coordinate selector retaining \(R\) and eliminating \(U=V\setminus R\), the formulas become \[\mu (x)=-Q_{UU}^{-1}Q_{UR}x,\qquad \operatorname {Cov}(\eta )=Q_{UU}^{-1}.\]
Proof.
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Proof.
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Orthogonality preserves the volume and gives \[\begin {aligned} \langle \phi ,Q\phi \rangle &=x^TAx+2x^TKy+y^TDy\\ &=x^TSx+(y+D^{-1}K^Tx)^TD(y+D^{-1}K^Tx). \end {aligned}\] The second term is minimized uniquely at \(y=\mu (x)\). Normalizing its density proves the conditional law at every \(x\). Writing \(v_x(y)=Bx+B_\perp y\), it also gives \[\begin {aligned} c_D&=\frac {(2\pi )^{(n-r)/2}}{\sqrt {\det D}},\\ \int _{\mathbb R^{n-r}}&e^{-\frac 12\langle v_x(y),Qv_x(y)\rangle } e^{-\lambda \mathcal V(v_x(y))}\,dy\\ &=c_De^{-\frac 12x^TSx}e^{-F_\lambda (x)}. \end {aligned}\] The last equality means equality after integrating each polynomial coefficient. Hence it retains the scalar and degree-zero terms as well as the induced polynomial vertices.
The moment series starts with \(1-\lambda a+\lambda ^2\mathbb E[\mathcal V^2\mid x]/2+O(\lambda ^3)\). Applying \(-\log (1+z)=-z+z^2/2+O(z^3)\) gives the displayed variance coefficient. In each moment, expand around \(h(x)\). Odd centered Gaussian moments vanish; every even moment is a sum over pairings, with one \(D^{-1}\) entry per pair in eliminated coordinates. This is [etingof2024mathematical, v2, Theorem 3.1 and Example 3.2, p. 29] applied to the positive form \(D\). For a quartic \(\mathcal V(\phi )=T(\phi ,\phi ,\phi ,\phi )/24\), the first coefficient has degrees \(4,2,0\). In the variance, pairings with no contraction between the two copies cancel, leaving degrees among \(6,4,2,0\), as shown in Proposition [fdip-0046]. All these claims concern finite polynomial coefficients.
For example, in full vertex coordinates with the symmetric four-tensor \(T_{ijkl}\) and \(h=h(x)\), Wick's three pairings give \[\begin {aligned} 24a(x)={}&T(h,h,h,h) +6\sum _{i,j,k,l}T_{ijkl}h_i h_j C_{kl}\\ &+3\sum _{i,j,k,l}T_{ijkl}C_{ij}C_{kl}. \end {aligned}\] The covariance \(C\) can be singular on \(H\): it is supported on \(Y\), and the formula follows by applying Wick to the nonsingular \(D^{-1}\) in eliminated coordinates. No nondegeneracy on all of \(H\) is needed for this expression.
TauCeti's
integral_mul_mul_mul_multivariateGaussian
proves the three-pairing identity for 4 centered Gaussian
coordinates, allowing repeated indices and any positive-semidefinite
covariance. Setting that covariance to \(C\) supplies the fourth-moment
step here. Contraction with \(T\), the formal logarithm and the
higher-moment pairing arguments are the additional deductions above.
The massive lattice analogue is [friedli2017statistical, Theorem 8.26, p. 399]: on a finite domain of \(\mathbb Z^d\), the conditional field has the massive harmonic mean determined by its boundary values and a killed-walk Green covariance. That theorem uses its normalized lattice Laplacian; it does not supply the arbitrary-projector argument above. The massless finite-domain analogue is Theorem 8.17, p. 391 of the same source.
A formal conditional-distribution counterpart in TauCeti is
condDistrib_multivariateGaussian.
It assumes a positive-semidefinite joint covariance and a
positive-definite observed covariance block, and identifies the
regular conditional law with an explicit Gaussian kernel almost
everywhere in the observed marginal. To specialize, order the
coordinates as \(Y\oplus X\) and use \(G=Q^{-1}>0\). From \(QG=I\),
\[\begin {aligned}
DG_{YX}+K^TG_{XX}&=0,\\
DG_{YY}+K^TG_{XY}&=I.
\end {aligned}\]
Therefore \(G_{YX}G_{XX}^{-1}=-D^{-1}K^T\) and
\(G_{YY}-G_{YX}G_{XX}^{-1}G_{XY}=D^{-1}\), the theorem's mean
and covariance formulas. Its almost-everywhere identification is
distinct from the explicit density kernel used here at every \(x\).
The perturbative logarithm and vertex calculations are proved above.
Example 11. Quartic vertices on an alternating cycle [fdip-0061]AGENTDRAFTED
- November 1, 2026
Example 11. Quartic vertices on an alternating cycle [fdip-0061]AGENTDRAFTED
- November 1, 2026
On the alternating four-cycle with weights \((1,t,1,t)\), take \(t>0\), \(m>0\) and \(s=1+t+m^2\). Retain \(R=\{0,2\}\), eliminate \(U=\{1,3\}\), and choose the interaction \[\mathcal V(\phi )=\frac {\phi _1^4+\phi _3^4}{24}.\] In retained coordinates \(x=(x_0,x_2)\), the conditional rules give independent eliminated noises of variance \(1/s\), with means \[\mu _1=\frac {x_0+tx_2}{s},\qquad \mu _3=\frac {tx_0+x_2}{s}.\] The first coefficient \(a(x)\) of the fiber interaction is \[\begin {aligned} a(x)={}&\frac {(1+t^4)(x_0^4+x_2^4)}{24s^4}\\ &+\frac {4t(1+t^2)(x_0^3x_2+x_0x_2^3) +12t^2x_0^2x_2^2}{24s^4}\\ &+\frac {(1+t^2)(x_0^2+x_2^2)+4tx_0x_2}{4s^3} +\frac 1{4s^2}. \end {aligned}\] Thus eliminating a quartic supported on the removed vertices creates quartic and quadratic couplings on the retained vertices, as well as a constant.
Proof.
- November 1, 2026
Proof.
- November 1, 2026
The eliminated precision is \(D=sI_2\); its cross block gives the two means above. For a centered real Gaussian \(\eta \) of variance \(v\), Wick's identity gives \(\mathbb E\eta ^4=3v^2\). Expanding \((\mu +\eta )^4\) and discarding odd moments yields \[\mathbb E(\mu +\eta )^4=\mu ^4+6\mu ^2v+3v^2.\] Consequently \[a(x)=\frac {\mu _1^4+\mu _3^4}{24} +\frac {\mu _1^2+\mu _3^2}{4s}+\frac 1{4s^2}.\] The binomial expansions of \((x_0+tx_2)^4+(tx_0+x_2)^4\) give coefficients \(1+t^4\), \(4t(1+t^2)\) and \(12t^2\). Squaring the two numerators gives \((1+t^2)(x_0^2+x_2^2)+4tx_0x_2\). Substitution proves every coefficient in the displayed polynomial.
In these named vertex coordinates, the ratio of the \(x_0^3x_2\) coefficient to the \(x_0^4\) coefficient is \[r(t)=\frac {4t(1+t^2)}{1+t^4}.\] It is unchanged by exchanging the two retained vertices and obeys \[\begin {aligned} r(t)&=r(1/t),&r(2)=r(1/2)&=\frac {40}{17},\\ r'(t)&=\frac {4(1-t^2)(1+4t^2+t^4)}{(1+t^4)^2}. \end {aligned}\] The derivative is positive for \(0<t<1\). Therefore \(r\) separates the parameter for \(0<t\le 1\), and on all positive parameters it separates exactly modulo \(t\leftrightarrow1/t\). This is a vertex-coordinate shape ratio, not an invariant of arbitrary orthogonal changes of the retained basis. The complete coefficient tensors instead obey the naturality theorem.
The reciprocal equality resembles that of the trace ratio, but neither equality gives a strict fixed-mass background isomorphism at reciprocal parameters. The edge-weight products are \(t^2\) and \(t^{-2}\), so a weight-preserving edge bijection would force \(t=1\). Also \(s\) changes at fixed mass; equality of the shape ratio does not make all induced coefficients equal.
A finite illustration uses \(t=2\), \(m=1\) and \(x=(0.5,-0.3)\). The means are \((-1/40,7/40)\), and the exact coefficient is \[a(x)=\frac {541201}{30720000}.\] The polynomial identity is the calculation above. The same conditional moment is the one exercised by the project script revision-checks.py.
Remark 12. How the selector changes induced interactions [fdip-0062]AGENTDRAFTED
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Remark 12. How the selector changes induced interactions [fdip-0062]AGENTDRAFTED
- November 1, 2026
A comparison of selectors should hold the full interaction fixed. On the alternating cycle of Example 11, now use \[\mathcal V_{\mathrm {tot}}(\phi )=\frac 1{24}\sum _{v=0}^3\phi _v^4.\] For opposite retained vertices \(R=\{0,2\}\), add \((x_0^4+x_2^4)/24\) to that example's first coefficient. The eliminated covariance is \(s^{-1}I_2\). For adjacent retained vertices \(R=\{0,1\}\), the eliminated coordinates are \((\phi _2,\phi _3)\), and \[\begin {aligned} D&=\begin {pmatrix}s&-1\\-1&s\end {pmatrix},\\ D^{-1}&=\frac 1{s^2-1}\begin {pmatrix}s&1\\1&s\end {pmatrix},\\ \mu (x)&=\frac {t}{s^2-1} \begin {pmatrix}1&s\\s&1\end {pmatrix} \begin {pmatrix}x_0\\x_1\end {pmatrix}. \end {aligned}\] Here \(s>1\), so the denominator is positive. These formulas follow from \(Q_{UR}=\left (\begin {smallmatrix}0&-t\\-t&0\end {smallmatrix}\right )\) and the conditional rules.
The adjacent first coefficient is explicitly \[\begin {aligned} a_{\mathrm {adj}}(x)={}&\frac {x_0^4+x_1^4}{24}\\ &+\frac 1{24}\sum _{j=2,3} \bigl (\mu _j(x)^4+6\mu _j(x)^2v+3v^2\bigr ),\\ v={}&\frac {s}{s^2-1}. \end {aligned}\] In particular its constant term is \(s^2/[4(s^2-1)^2]\), whereas the opposite selector gives \(1/(4s^2)\). They are unequal for \(s>1\). The adjacent propagator also has a nonzero cross entry \(1/(s^2-1)\) between eliminated vertices, which contributes pairings between their quartics in the variance coefficient. Such a cross entry is absent for opposite elimination. Thus the same precision and full interaction yield different conditional contraction rules and induced vertices when the selector changes.
For a restricting selector, \(PQ=QP\) makes the cross block \(K\) zero. The Gaussian law then factors between \(X\) and \(Y\), and the eliminated conditional mean is zero. Any remaining coupling between the two subspaces comes from \(\mathcal V\), not from a Gaussian cross term. In particular, if \(\mathcal V(x+y)=\mathcal V_X(x)+\mathcal V_Y(y)\), then \[F_\lambda (x)=\lambda \mathcal V_X(x) -\log \mathbb E_{N(0,D^{-1})}e^{-\lambda \mathcal V_Y}.\] The second term is a formal scalar independent of \(x\). If the interaction has mixed terms, new retained vertices can still be induced even though the Gaussian selector restricts. Group-algebra selectors restrict under the stated equivariance hypothesis; a subrepresentation selector need not.
These formulas answer the finite perturbative question: the propagator and induced interaction depend on the supplied background and selector. They do not establish convergence of the formal series, a continuum interacting measure, or a physical mechanism that selects the background.