The GNS translation representation of a positive-definite function #
A positive-definite function on an additive commutative group has a canonical Hilbert space, obtained from its translation-invariant positive-definite kernel. Translation of the kernel vectors extends uniquely to a unitary operator. These operators form a group representation, and the original function is a matrix coefficient of its vector at zero.
The translation action is part of the GNS/Kolmogorov decomposition of a positive-definite function. Its later use in LCA Bochner theory requires spectral measures and Pontryagin duality.
References #
- W. Rudin, Fourier Analysis on Groups (1962), Chapter 1.
The canonical Hilbert space of the translation-invariant kernel K(a,b) = F(a-b).
Equations
- hF.gnsSpace = ⋯.KolmogorovSpace
Instances For
The vector in the GNS space corresponding to a group element.
Equations
- hF.gnsVector a = ⋯.kolmogorovFeature a
Instances For
The inner product of two GNS vectors is the original positive-definite kernel.
Squared distances between GNS vectors are controlled by the real part of the function.
The GNS vectors span a dense subspace.
Translation by g is a unitary operator on the canonical GNS Hilbert space. Its action on
the dense family of kernel vectors is v(a) ↦ v(g+a).
Equations
- hF.gnsTranslation g = ⋯.kolmogorovEquiv (fun (a : G) => hF.gnsVector (g + a)) ⋯ ⋯
Instances For
Translation acts on the canonical GNS vectors by addition.
The translation at zero is the identity operator.
GNS translations compose according to the group law.
The GNS representation as a homomorphism from the multiplicative copy of G to the
unitary operators on its canonical Hilbert space.
Equations
- hF.gnsRepresentation = MonoidHom.mk' (fun (g : Multiplicative G) => hF.gnsTranslation (Multiplicative.toAdd g)) ⋯
Instances For
The representation operator at g is translation by g.
The representation translates each GNS vector.
A positive-definite function is a matrix coefficient of its canonical unitary representation.
Continuity of a positive-definite function at zero makes its canonical feature map continuous. This is the regularity needed for a strongly continuous representation.
The GNS translation representation is strongly continuous: every vector has a continuous orbit.
The canonical unitary representation has continuous orbits.