1. Introduction and Preliminaries
Let
be a separable Hilbert space and
a dense domain in
, equipped with a locally convex topology
, finer than the norm topology. Let
S be an essentially self-adjoint operator in
which maps
into
continuously.
S has a continuous extension
given by the conjugate duality (the adjoint, in other words; i.e.,
) from the conjugate dual space
into itself. A
generalized eigenvector of
S, with eigenvalue
, is then an eigenvector of
; that is, a conjugate linear functional
such that:
which we rewrite as
. The completeness of the set
} is expressed through the Parseval identity:
where the positive measure
may be, in general, both discrete and continuous.
In the present context, the Gel’fand–Maurin theorem [
1] states that if
is a nuclear domain in the Hilbert space,
and
S is an essentially self-adjoint operator in
which maps
into
continuously, then
S admits a
complete set of generalized eigenvectors.
Let
A now be a self-adjoint operator with dense domain
in a
separable Hilbert space
, which can be represented as
with
an orthonormal basis of
. The notion of
simple spectrum for the operator
A corresponds to the fact that each eigenvalue has multiplicity 1. Of course this definition is of little use when the operator
A has (also or only) a continuous spectrum. To cover this case a different definition has been proposed which relies on the notion of
generating vector [
2] (Definition 5.1) or [
3] (§69). Let us denote by
the spectral measure associated with the self-adjoint operator
A.
Definition 1. A vector is called a generating vector or cyclic vector for A if the linear span of vectors , (the σ-algebra of Borel sets of the real line), is dense in . We say that A has a simple spectrum if A has a generating vector.
This definition is motivated by the fact that for operators of the form (
1) one can find such a generating vector quite easily; in fact, the vector
achieves this. This can also be expressed by stating that the von Neumann algebra
generated by the spectral resolution
,
of
A is maximal abelian; i.e.,
. [Recall: An abelian von Neumann algebra in separable Hilbert space is maximal abelian if and only if it has a generating vector: [
4] (Cor. 5.5.17; Cor. 7.2.16)].
In this paper, we will study the spectral behavior of self-adjoint operators
A which are represented in the form
where the
’s are generalized eigenvectors. In particular, we determine the relationship between the spectral family of
A and the sesquilinear forms defined by the
’s. Moreover, we discuss the notion of
simple spectrum in this situation; in other words, we expect to find conditions for every subspace
of generalized eigenvectors, corresponding to
, to be one-dimensional. An interesting tool in this context is that of
Gel’fand distribution basis, introduced recently in [
5] and defined in
Section 3. In
Section 4, we will see how this notion may be utilized for characterizing the class of operators we consider.
Before going further, we fix some notations and give some preliminary results on self-adjoint operators.
Let A be a self-adjoint operator with dense domain . As usual we denote by , , the point spectrum (i.e., the set of true eigenvalues), the continuous spectrum and the residual spectrum of an operator A, respectively. If A is self-adjoint then and
If the spectrum of
A contains some
true eigenvalue
, then the spectral measure of the singleton
is given by the jump of the spectral function
(remember that the spectral family is strongly right continuous, i.e.,
, for every
and every
. This can be deduced from the following theorem which describes the spectrum of a self-adjoint operator
A in terms of its spectral family [
3] (Ch.6, §68).
Proposition 1. Let be the spectral family of the self-adjoint operator A and a real number. Then,
- (i)
if, and only if, is constant in a neighborhood of .
- (ii)
is an eigenvalue if, and only if, is discontinuous in .
- (iii)
if, and only if, is continuous in , but non constant in every neighborhood of .
As a consequence, a real number belongs to the spectrum of A if, and only if, , for every .
Remark 1. Since the Hilbert space is separable, the point spectrum of A consists at most of a countable set of true
eigenvalues. Therefore, the continuous spectrum is a Borel set and, for every , the restriction of the spectral measure to may be, for certain f, absolutely continuous with respect to the Lebesgue measure (restricted to the same set). Indeed, according to [6] (Ch.VII.2) one could consider a different decomposition of the spectrum into pure point , continuous , absolutely continuous and singular spectrum (these sets need not be disjoint). Corresponding to this, the Hilbert space decomposes as where , , are, respectively, the pure point, absolutely continuous and singular parts of obtained via the corresponding Lebesgue decomposition of the measures generated by the functions , . We will not go into further details on the spectral analysis of self-adjoint operators, for which we refer to [2,3,6]; for a discussion of this matter in the framework of RHS’s, see also [7]. 3. Gel’fand Distribution Bases
Let be a measure space with a -finite positive measure. a weakly measurable map; i.e., we suppose that, for every , the complex valued function is -measurable. Since the form which puts and in conjugate duality is an extension of the inner product of , we write for , .
We will say that
- (i)
is total if, and for almost every implies ;
- (ii)
is μ-independent if the unique measurable function on such that , for every , is -a.e.
We recall the following definitions and facts from [
5].
Definition 2. Let be a rigged Hilbert space and be a weakly measurable map from into . We say that ζ is a Gel’fand distribution basis
if ζ is μ-independent and satisfies the Parseval identity: i.e., By (
4), it follows that if
and
is a sequence of elements of
, norm converging to
g, then the sequence
, where
, is convergent in
. Put
. The function
depends linearly on
g, for each
. Thus, a linear functional
on
can be defined by
For each
,
extends
, but
need not be continuous, as a functional on
.
Moreover, in this case, the sesquilinear form
associated with the quadratic form (
4); i.e.,
is well defined on
, it is bounded with respect to
and possesses a bounded extension
to
.
The following result describes the behavior of Gel’fand distribution bases.
Corollary 1. Let ζ be a Gel’fand distribution basis. The following statements hold.
- (i)
For every , there exists a unique function such that In particular, if , then μ-a.e.
- (ii)
For every fixed , the map defines as in (5) a linear functional on and
As proved in [
5] (Proposition 3.15) the
synthesis operator
defined by
is an isometry of
onto . Its adjoint
associates to each vector
the function
whose existence is guaranteed by (i) of the previous corollary.
4. Operators Constructed from Gel’fand Distribution Bases
4.1. Construction of the Operators
Let be a rigged Hilbert space. We suppose that the space is reflexive under its topology . In this section, we consider the measure space , where denotes the Lebesgue measure. Let be a Gel’fand distribution basis.
Consider the following operator
A (see also, [
5] (Section 4)):
where the last equality is intended as a conjugate linear functional on
:
It is easily seen that
is bounded; thus by a limiting procedure we can extend it to
, where we get
Then, we have
Lemma 1. is a self-adjoint operator in .
Proof. Let
; then there exists
such that
if, and only if,
and
; i.e., if, and only if,
and
. □
In a similar way, if
u is a Borel function on
, we define
If is dense, then is the operator corresponding to , the conjugate of u.
Lemma 2. The operator is bounded if, and only if, .
Proof. The sufficiency is immediate. The necessity follows from the fact that the analysis operator is also invertible; thus for every . This, in turn, implies that . □
If u is bounded, then and is bounded.
Let us suppose that
implies
. Then, it makes sense to compute
, which is obviously given by
On the other hand
by definition of a generalized eigenvector. Thus, we have
Remark 2. However, we cannot write the lhs as since this is undefined, in general.
Since
A is self-adjoint, its spectrum
is real. Using (
8) and (
9), we find that for every
, the resolvent operator
is given by
Remark 3. By Lemma 1, the operator A is self-adjoint; then it has a spectral decompositionThe previous equality and (7) provide strong clues to believe that there is a sort of relationship of absolute continuity of the measure defined by , and the Lebesgue measure. This is not too far from truth; however, a more precise analysis must be undertaken. We begin with proving that, as expected, the operator A defined in (7) has only a continuous spectrum.
Lemma 3. Let A be the operator defined in (7). Then, for every the operator is injective and is a proper dense subspace of ; i.e., .
Proof. Let
be a solution of
=0. Then
Since
varies in
one must have
a.e.; this shows that
. Moreover, the residual spectrum of
A is empty (because
A is selfadjoint). Then necessarily
is dense in
. The inverse of
is given by (
10) with
and by Lemma 2 it follows that
is unbounded if
. □
Theorem 1. Let be the canonical RHS associated with a self-adjoint operator A. For every the function is almost everywhere differentiable and there exists a positive operator such that Proof. Let . The function is nonnegative and increasing. Then, it is differentiable almost everywhere, by Lebesgue’s differentiation theorem; the derivative, of course, depends on f.
Using the polarization identity, we conclude that the function
,
, which is bounded variation, is also differentiable a.e. Put
It can be easily seen that
is a sesquilinear form in
. Now, from the usual spectral calculus one deduces that, for every
,
, for each
and the following inequality holds.
This implies that for each
,
(which maps
into
) is continuous from
into itself. This, in turn implies that, for almost every
,
depends continuously on
. By symmetry, it is separately continuous; however, since
is a Fréchet space, it is jointly continuous; i.e., for almost every
there exists
and
such that
In this case [
8] (Ch.10), there exists an operator
such that
□
On the basis of the previous considerations we expect that in the case of the operator
A defined by a Gel’fand distribution basis, we should have
Let us first examine some simple yet significant examples.
Example 1. In the RHS , let us consider the operator Q of multiplication by x, i.e.,This operator is the restriction to of the multiplication operator by x defined onThis operator has a Gel’fand basis of generalized eigenvectors given by the distributions , the Dirac delta centered at λ. Indeed,which can be read as The spectral resolution of Q, on the other hand, can be written aswhere , with the characteristic function of . It is clear that we can also write Example 2. In the RHS let us consider the momentum operator P, i.e.,This operator is the restriction to of the momentum operator defined on , the familiar Sobolev space. This operator has a Gel’fand basis of generalized eigenvectors given by the regular distributions . The use of Fourier transform on the computations of Example 1 shows thatwhere denotes the Fourier transform of g. The previous example motivates the conjecture that if
A is a self-adjoint operator given in terms of some Gel’fand basis
as in (7) and
is the spectral family of
A, then the spectral family
of
A should be given by
In order to study this problem, we begin with considering for
the sesquilinear form
where
is the characteristic function of
. Then
is a bounded sesquilinear form on
, with respect to the norm of
and hence there exists a bounded operator
such that
. A Gel’fand distribution basis
is characterized by the fact that the synthesis operator
takes values in
and it is an isometry of
onto
[
5] (Proposition 3.15); thus its inverse (or adjoint)
(i.e., the analysis operator) is also isometric. Let us denote by
the multiplication operator by
(the characteristic function of
), then
is represented by the operator
which is a projection operator that we denote by
.
Then the equality
implies that
The uniqueness of the spectral measure implies that
, for almost every
. These considerations prove the following theorem.
Theorem 2. Let A be the self-adjoint operator defined by a Gel’fand distribution basis ζ, in the sense thatThen, the spectral measure of A is absolutely continuous with respect to the Lebesgue measure and Remark 4. Replacing with , the previous equality extends to
Remark 5. As a consequence of Theorem 2, in the case under consideration, we conclude that the operators defined in (8), (9) satisfy the usual rules of the functional calculus for unbounded self-adjoint operators. In particular, if are Borel functions on , one has 4.2. Simple Spectrum
In both examples considered above, every generalized eigenvalue has multiplicity 1; i.e., the subspace of generalized eigenvector corresponding to every generalized eigenvalue has dimension 1. In finite-dimensional spaces, this is exactly the definition of the operator having simple spectrum.
Let us now discuss in general terms how the notion of simple spectrum can be handled in this context.
Given a self-adjoint operator A, we consider the canonical rigged Hilbert space associated with A. We denote by the subspace consisting of all generalized eigenvectors corresponding to the generalized eigenvalue . For all , one can define a linear functional on by for all . On the other hand, it is easily seen that every continuous functional on has the form for some . We denote by the space of all these functionals. The correspondence defined by is called the spectral decomposition of the element f corresponding to A. If for every generalized eigenvalue , implies then A is sometimes said to have a complete system of generalized eigenvectors. However, this condition is not sufficient to guarantee that generalized eigenvectors are a Gelf’and distribution basis and will not be adopted for this reason.
As noted before, in finite dimensional spaces, an operator is said to have a simple spectrum if each eigenvalue has multiplicity 1. In the infinite dimensional case, this notion is useless due to the possible presence of a continuous part of the spectrum. If
A is a bounded self-adjoint operator in
, then one says that
is
generating if the set
is dense in
. As is known, this is equivalent to say that the von Neumann algebra
generated by
A is maximal abelian (i.e., it coincides with its commutant
. These facts, in turn, are equivalent to
A being unitarily equivalent to a multiplication operator: Assume in fact that
A has a generating vector
and put
,
, the
-algebra of Borel sets on the real line. There then exists a unitary operator
with
A similar result can be obtained if
A is self-adjoint but not necessarily bounded [
2] (Section 5.4). In this case, we prefer to call
cyclic a vector
which lies in the space
and such that the set
is total in
(see also [
18] (Section 3.5); in [
3] (Sect. 69) and [
2] (Section 5.4) the notion of generating vector is always considered in the context of the von Neumann algebra that the operator generates).
For our purposes, it seems more convenient to strengthen the definition as follows.
Definition 3. We say that a vector is strongly cyclic (for A) if the set is total in or equivalently if is dense in .
It is easily seen that a strongly cyclic vector is cyclic. By [
2] (Proposition 5.20), a generating vector exists if and only if a cyclic vector for
A exists.
Theorem 3. Let A be a self-adjoint operator and be a RHS such that . If there exists a strongly cyclic vector for A, then every subspace is one-dimensional.
Proof. Suppose that
is not one-dimensional and let
be linearly independent functionals in
. Then there exists a nonzero vector
such that
and
. Then if
is a cyclic vector for
A, there exists a sequence
of polynomials such that
. Then,
this implies that
The first equality above implies that
; thus, the second one entails that
. However, since
is strongly cyclyc this, in turn, implies that
is identically 0 on
. A contradiction. □
We conjecture that the converse implication holds true, but we could not prove it.