Results on Spectral Measures and an Application to a Spectral Theorem ()
1. Introduction
One of the main aims of functional analysis is to study the spectra of various operators on Hilbert spaces [1]-[3]. Some basic mathematical properties of Hilbert spaces and spectral measures are developed and used to generalize a particular version of a spectral theorem in infinite dimensions. The study of the spectral properties of operators has always been of great interest since such results have applications in other areas of science [4] [5]. This subject has come up again in differential geometry, such as Ricci flow [6]-[10]. Moreover, there are numerous applications of these theorems and other ideas from functional analysis all through quantum mechanics as well.
Let
denote the set of complex-valued continuous functions on the space
and
the subset of functions of
which go to zero at infinity. Suppose
is a set with a specified Boolean
-algebra
of subsets. A spectral measure is a function
whose domain is
and whose values are idempotent Hermitian operators usually called projections which satisfy
and
whenever
is a disjoint sequence of sets in
. Such a set
and
-algebra
of subsets is called a measurable space,
. An example of a spectral measure is to take
a measure space with measure
and Hilbert space
and write
where
denotes the characteristic function of
with
and
. If
is a spectral measure then
and
is finitely additive.
It can be shown that a projection-valued function
on the class of measurable subsets of a measure space
is a spectral measure if and only if
, and moreover for each pair of vectors
and
, the complex-valued set function
defined for any
in
by
is countably additive.
Some important theorems are required at the end in the proof of the spectral theorem and they are stated now so they may be used later. First, the Stone-Weierstrass theorem states that for a given compact set
, suppose
is an algebra of continuous functions
closed under conjugation and separates points of
and there are no elements of
on which all functions in
vanish, then
is dense in
. The Riesz theorem also appears: if
is a locally compact Hausdorff space and
is a bounded linear functional on
, there is a unique complex Borel measure
such that
and
.
Let
be a Hilbert space such that for any
in
, the inner product is written
. A star on the inner product or other complex object denotes complex conjugation whereas on an operator
it represents the adjoint
.
Lemma 1. If
is a linear functional on
, then
for a particular choice of
in
.
Proof. Let
then it suffices to prove that
, since if not, take
. Then
is a nonzero vector in
and can be normalized by setting
in
, so
. Given any
, set
where
and so
. Evaluating
, we get
This implies that
, and since
, it is the case that
◻
Definition 1. A bounded linear functional is a map
which is linear in the first term, conjugate linear in the second and such that there exists a non-negative constant denoted
which satisfies
(1)
Lemma 2. If
is a bounded bilinear functional on
, then there exists a unique operator
on
such that
for all
.
Proof. Let
be fixed and nonzero and let
. By Lemma 1, there exists a vector denoted
such that
. This implies that
(2)
Since
is a linear functional,
has to be a linear transformation. It suffices to verify that
is bounded. Set
in (2) to obtain
or dividing out
. This implies that
◻
Lemma 2 is often referred to as the Riesz lemma.
2. Projections
Both the adjoint of an operator and projection operators as well play an important role here.
Theorem 1. Let
be an operator, then there exists a unique operator
called the adjoint of
which satisfies
for all
.
Proof. Set
. Then
is a bounded, bilinear functional Hence there exists a unique operator
such that
. This must agree with the preceding expression which implies that
. ◻
Definition 2. Operator
is Hermitian if
, and it is normal if
.
Proposition 1. An operator
is normal if, and only if
.
Proof. Take an arbitrary vector
, then by the definition of normal operator,
. This implies that
from which we conclude that
. ◻
Riesz lemma 2 implies that, if
is symmetric
, then the resulting operator will be Hermitean.
Definition 3. If
is a closed subspace of
, then Hilbert space theory states that every vector
has a unique decomposition
, where
and
. The projection on
is defined to be the mapping
. Note that
is necessarily an operator. If
is
, denote
by 1 and if
, define
as 0. ◻
Definition 4. Let the set
be projections onto
respectively. These can be partially ordered by
if
. Further, the projection onto the space
can be defined as
. ◻
Theorem 2. An operator
is a projection if and only if it is Hermitian and idempotent, so
.
Proof. Suppose
is a subset of
and projection
projects to the subspace
and for
we have
. Thus
so
which gives
. Let
and
in
such that
and
. It follows that, since
and
,
This implies
is Hermitian. ◻
Lemma 3. Suppose that
is Hermitian and idempotent. Define the space
. Then
is a projection onto
.
Proof. To show this, it suffices to prove that, for all
,
. Expanding the left-hand side then, since
is assumed Hermitian, we have
◻
Corollary 1. If
is a projection, then for all
,
.
Proof.
◻
3. Spectral Measures
The final theorem is concerned with spectral measures, and so these are studied now. Let
be the set of Borel sets in
and
the set of projections on the Hilbert space
.
Definition 5. A complex spectral measure is a function
which has the following properties: 1)
and
, 2) If
is a family of disjoint Borel sets, then
. ◻
It is the case that if
, then
. Spectral measures also have the property that
. Act on both sides of this with
and use the corrolary and observation
to arrive at
or
, which simplifies to
.
Proposition 2. Suppose
is any operator such that for all
, the related function satisfies
,
and
. Then
is a spectral measure.
Proof. Suppose
is a disjoint family of Borel sets in
, then
The sequence
is therefore summable. For all disjoint Borel sets
and vectors
,
The statement follows simply by examining the partial sums of
. ◻
Definition 6.
represents an arbitrary fixed measurable space and
the class of all complex-valued bounded measurable functions on
.
when
. The expression spectral measure always refers to a spectral measure in
.
Definition 7. Let
be a spectral measure then the spectral measure with respect to vectors
of a measurable function
is defined to be the Lebesgue-Stieltjes integral given by
(3)
The spectrum of a spectral measure
is denoted by
. The union is taken over all open sets
which satisfy the condition
. If the set
is compact, we say
is compact. Since
, when
is changing,
means
must overlap
to yield a nonzero contribution. ◻
Theorem 3. Let
and
be complex valued, bounded, measurable functions on
and
a spectral measure, then
(4)
Proof. Suppose we denote the integrals as
If the complex measure
in
is defined for every set
in
by writing
with
fixed vectors in
, for every set
in
we have
It follows that,
The conclusion is that
has to be equal to the integral
. ◻
The next theorem makes an important connection between the spectral integral and a unique operator
which is normal.
Theorem 4. Suppose
is a compact spectral measure, then there is a unique normal operator
such that for all vectors
(5)
This is often expressed by writing
.
Proof. Let
which is finite for all
as the set
is compact. Moreover,
is a bilinear functional and
is bounded as shown by working out
where
. Applying the parallelogram law, the following bound is obtained
Thus
can be computed by evaluating the supremum of this under the constraint
. This implies that
, so
is bounded. By the Riesz lemma, a unique operator
must exist.
It remains to show that
is a normal operator. Construct an operator called
along similar lines by means of the integral
(6)
It follows that
Since the adjoint is unique, it must be that .
Suppose a Borel set is taken, then we can calculate, for any elements
,
This implies that
(7)
In a similar manner, we calculate
(8)
Comparing the two results (7) and (8), it follows that
. Since
are arbitrary, this implies that
, so
is a normal operator. ◻
Theorem 4 is related to a theorem which is just stated and not proved here: If
is a spectral measure and if
, then there exists a unique operator
such that
for every pair of vectors
and
in the space.
4. Operators and Spectra
Let us define what is meant by the spectrum of an operator. To get to the form of the theorem we want to prove, it is necessary to generalize the concept of eigenvalue. This often comes up very often in the study of both finite dimensional vector spaces as well as infinite.
Definition 8. The spectrum of an operator
is defined to be the set of numbers
such that the operator
is not invertible. ◻
In order to use the definition, a characterization of invertible and non-invertible operators is required, and leads into the following theorem.
Theorem 5. An operator
on
is invertible if the image of
is dense in
and as well there exists an
such that for all
,
is bounded from below
(9)
Proof. First suppose that
is invertible. The image of
is all of
, which is dense in
. Let us define
, and so for all
,
Dividing this on both sides by
gives
.
Suppose the range of
is dense in
and there exists a real number
such that
. It has to be shown that the range of
is
. It suffices to show that it is closed. Suppose that the set
is a Cauchy sequence in the range of
. For all
, choose
such that
, then since
is linear,
by hypothesis, dividing by
,
This result implies that
is a Cauchy sequence whenever
is. Hence,
must converge to a
. By continuity,
has to approach
, which implies the range is closed.
To prove that
is injective, notice that if
, then
. From this it follows that
, and so
is bijective. The inverse operator
is also linear, so it suffices to show that
is a bounded operator. This is just a consequence of the fact that
which implies that
◻
Proposition 3. If
is any operator which satisfies the condition
, then
is invertible.
Proof. Set
. Then for any
, we find
This implies that
is bounded from below.
Let
be the range of operator
in
. Define the constant
. It suffices to prove that
. Suppose this is not the case. Since
, there exists a
and
such that
(10)
It follows then that since we have taken
, using (10), we estimate that
(11)
Since
is assumed to be nonzero, a contradiction has been obtained. ◻
Theorem 6. If
is an operator then
is compact and if
, then
.
Proof. It is proved that
is closed so the first statement follows from the second. Suppose
is not an element of
. Let
be such that
and that
. Then we have
By Proposition 3, the operator
is invertible in a ball of radius
about
. It may be concluded that
is open.
Now suppose that
so we have
which, after adding and subtracting
is equivalent to
. This implies that
is invertible. Multiply this operator by the scalar
and it follows that
is invertible and
is not an element of
. ◻
Theorem 7. If
is a compact spectral measure and
, then
.
Proof. Suppose that
, so
is open by definition. Hence there exists
such that
with
. Since
is a spectral measure,
and so for any
,
Consequently, the operator
is bounded from below.
It remains to prove that the image of the operator
is dense in
. Suppose that
is any normal operator which is bounded from below and has range
. It suffices to show that
. Suppose
is nonempty set, so there is a
such that
. Then for all
, it follows that
and hence
This implies that
. Moreover, since operator
is bounded below, there exists
such that
This means
, hence the operator
is invertible so
and
.
Suppose now that
. Choose a
such that
. For any
,
However,
is arbitrary, so
is not bounded from below. Hence it cannot be invertible, which means
. It follows that
. Combining these two results,
and
, it is concluded that
. ◻
5. Spectral Theorem
Let
be the set of complex-valued continuous functions on
and
the subset of
of functions which approach zero at infinity. A spectral theorem is now developed from what has been proved so far. In the process, the Stone-Weierstrass and Riesz theorems are used.
Theorem 8. Let
be a Hermitean operator so
. Then there exists a spectral measure
such that for all
.
(12)
Proof. Let
and
be two fixed vectors in
and let
be a given polynomial which is used to define a functional
,
(13)
This functional is bounded above,
where
. Since
is compact, the Stone-Weierstrass theorem can be used to show these polynomials are dense in
. Therefore,
defines a bounded linear functional on all of
. Hence there exists a unique complex measure
such that, by the Riesz theorem,
(14)
Take a Borel set
and use it to define
, so
is a bounded symmetric bilinear form. By the Riesz lemma, there exists a unique Hermitean operator
such that
(15)
So from (14), we have
(16)
Now let
and set
in (16),
This implies that
. Next set
and substitute it into (16) to obtain the important result valid for all
(17)
This can be expressed in an equivalent way,
(18)
It is sufficient to check that
is projection valued so it can be said to be a spectral measure. It is known that
is Hermitian, so it suffices to verify that it is an idempotent operator. To this end, let
and
be arbitrary polynomials and define a measure
as
(19)
Then based on the construction of
it follows that
Since
was chosen arbitrarily, the Stone-Weierstrass theorem and combined with the fact that compactly supported continuous functions are dense in
, replacing
by the characteristic function
, leads to the conclusion that for all Borel sets
,
The Stone-Weierstrass theorem can be employed once again, but with respect to
. So using the fact
is idempotent, it follows that
Since
are arbitrary elements of
, result (12) of the theorem follows. ◻
6. Conclusions
The spectral theorem has become a major part of functional analysis and not without reason, as it has many applications in science such as in quantum mechanics. It comes in different versions depending on such things as compactness of the operator and dimension of the space. Some introductory theorems from functional analysis have been proved related to functional, projectors, adjoints and spectra. Some new proofs have been given concerning spectral measure and spectra which results in the second last section in a proof of a particular form of a spectral theorem.