Functional determinant

Functional determinant

In mathematics, if "S" is a linear operator mapping a function space V to itself, it is possible to define an infinite-dimensional generalization of the determinant in some cases.

The corresponding quantity det("S") is called the functional determinant of "S". There is a "functional integral definition" of it and the functional Pfaffian Pf:

:frac{1}{sqrt{mbox{det}(S)=frac{1}{mbox{Pf}(S)}propto int_{V} ! mathcal{D} phi ; e^{-langle phi | S|phi angle}

in analogy with the finite dimensional case.

The argument of the exponential inside the integral is written in Dirac notation and its meaning is the scalar product between the (ket) vector |phi angle and the vector S|phi angle, which is the result of applying the operator S to the vector |phi angle.

Motivation for the terminology comes from the following. We shall consider the case when "S" possesses only discrete spectrum {lambda_i} with a corresponding complete set of eigenvectors {f_i} and let further the spectrum consist of a discrete set of points (as would be the case for the second derivavtive operator on a compact interval Omega). The functional measure mathcal{D}phi is equivalent to the spectral measure associated with "S" which in this case is simply the scaled product Lebesgue measure mathcal{D}phi=prod_i df_i/(2pi) (which would be an infinite product in infinite dimensional spaces such as L^{2} [Omega] ). Hence the inner product in the exponential is written as

:langlephi|S|phi angle=sum_{i,j}langle phi|f_i anglelangle f_i|S|f_j anglelangle f_j|phi angle= sum_iphi_i^2lambda_i

where phi_i are the components of the vector phi in the spectral basis and the integral becomes Gaussian

: int mathcal{D} phi e^{-langle phi | S|phi angle}=int prod_ifrac{ df_i}{2pi} e^{-sum_i phi_i^2lambda_i}

which evaluates to

: int mathcal{D}phi e^{-langlephi|S|phi angle}proptoleft( prod_i lambda_i ight)^{-1/2}.

Hence as in finite dimensions, the generalized determinant may be interpreted as the product of the eigenvalues.


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