Spectrum (functional analysis)

Spectrum (functional analysis)

In functional analysis, the concept of the spectrum of a bounded operator is a generalisation of the concept of eigenvalues for matrices. Specifically, a complex number λ is said to be in the spectrum of a bounded linear operator T if λI − T is not invertible, where I is the identity operator. The study of spectra and related properties is known as spectral theory, which has numerous applications, most notably the mathematical formulation of quantum mechanics.

The spectrum of an operator on a finite-dimensional vector space is precisely the set of eigenvalues. However an operator on an infinite-dimensional space may have additional elements in its spectrum, and may have no eigenvalues. For example, consider the right shift operator R on the Hilbert space 2,

(x_1, x_2, \dots) \mapsto (0, x_1, x_2, \dots).

This has no eigenvalues, since if Rxx then by expanding this expression we see that x1=0, x2=0, etc. On the other hand 0 is in the spectrum because the operator R − 0 (i.e. R itself) is not invertible: it is not surjective since any vector with non-zero first component is not in its range. In fact every bounded linear operator on a complex Banach space must have a non-empty spectrum.

The notion of spectrum extends to densely-defined unbounded operators. In this case a complex number λ is said to be in the spectrum of such an operator T:DX (where D is dense in X) if there is no bounded inverse (λI − T)-1:XD. If T is a closed operator (which includes the case that T is a bounded operator), boundedness of such inverses follow automatically if the inverse exists at all.

The space of bounded linear operators B(X) on a Banach space X is an example of a unital Banach algebra. Since the definition of the spectrum does not mention any properties of B(X) except those that any such algebra has, the notion of a spectrum may be generalised to this context by using the same definition verbatim.


Spectrum of a bounded operator

The spectrum of a bounded linear operator T acting on a Banach space X is the set of complex numbers λ such that λI − T does not have an inverse that is a bounded linear operator. If λI − T is invertible then that inverse is linear (this follows immediately from the linearity of λI − T), and by the bounded inverse theorem is bounded. Therefore the spectrum consists precisely of those λ where λI − T is not bijective.

The spectrum of a given operator T is denoted σ(T), and the resolvent set (the set of points not in the spectrum) is denoted ρ(T).

Basic properties

The spectrum of a bounded operator T is always a closed, bounded and non-empty subset of the complex plane.

If the spectrum were empty, then the resolvent function

R(\lambda) = (\lambda I - T)^{-1} \,

would be defined everywhere on the complex plane and bounded. But it can be shown that the resolvent function R is holomorphic on its domain. By the vector-valued version of Liouville's theorem, this function is constant, thus everywhere zero as it is zero at infinity. This would be a contradiction.

The boundedness of the spectrum follows from the Neumann series expansion in λ; the spectrum σ(T) is bounded by ||T||. A similar result shows the closedness of the spectrum.

The bound ||T|| on the spectrum can be refined somewhat. The spectral radius, r(T), of T is the radius of the smallest circle in the complex plane which is centered at the origin and contains the spectrum σ(T) inside of it, i.e.

r(T) = \sup \{|\lambda| : \lambda \in \sigma(T)\}.

The spectral radius formula says[1] that for any element T of a Banach algebra,

r(T) = \lim_{n \to \infty} \|T^n\|^{1/n}.

Classification of points in the spectrum of an operator

A bounded operator T on a Banach space is invertible, i.e. has a bounded inverse, if and only if T is bounded below and has dense range. Accordingly, the spectrum of T can be divided into the following parts:

  1. λσ(T), if λ - T is not bounded below. In particular, this is the case, if λ - T is not injective, that is, λ is an eigenvalue. The set of eigenvalues is called the point spectrum of T and denoted by σp(T). Alternatively, λ - T could be one-to-one but still not be bounded below. Such λ is not an eigenvalue but still an approximate eigenvalue of T (eigenvalues themselves are also approximate eigenvalues). The set of approximate eigenvalues (which includes the point spectrum) is called the approximate point spectrum of T, denoted by σap(T).
  2. λσ(T), if λ - T does not have dense range. No notation is used to describe the set of all λ, which satisfy this condition, but for a subset: If λ - T does not have dense range but is injective, λ is said to be in the residual spectrum of T, denoted by σr(T) .

Note that the approximate point spectrum and residual spectrum are not necessarily disjoint (however, the point spectrum and the residual spectrum are).

The following subsections provide more details on the three parts of σ(T) sketched above.

Point spectrum

If an operator is not injective (so there is some nonzero x with T(x) = 0), then it is clearly not invertible. So if λ is an eigenvalue of T, one necessarily has λ ∈ σ(T). The set of eigenvalues of T is also called the point spectrum of T, denoted by σp(T) .

Approximate point spectrum

More generally, T is not invertible if it is not bounded below; that is, if there is no c > 0 such that ||Tx|| ≥ c||x|| for all xX. So the spectrum includes the set of approximate eigenvalues, which are those λ such that T - λ I is not bounded below; equivalently, it is the set of λ for which there is a sequence of unit vectors x1, x2, ... for which

\lim_{n \to \infty} \|Tx_n - \lambda x_n\| = 0.

The set of approximate eigenvalues is known as the approximate point spectrum, denoted by σap(T).

It is easy to see that the eigenvalues lie in the approximate point spectrum.

Example Consider the bilateral shift T on l2(Z) defined by

T(\cdots, a_{-1}, \hat{a}_0, a_1, \cdots) = (\cdots, \hat{a}_{-1}, a_0, a_1, \cdots)

where the ˆ denotes the zero-th position. Direct calculation shows T has no eigenvalues, but every λ with |λ| = 1 is an approximate eigenvalue; letting xn be the vector

\frac{1}{\sqrt{n}}(\dots, 0, 1, \lambda^{-1}, \lambda^{-2}, \dots, \lambda^{1 - n}, 0, \dots)

then ||xn|| = 1 for all n, but

\|Tx_n - \lambda x_n\| = \sqrt{\frac{2}{n}} \to 0.

Since T is a unitary operator, its spectrum lie on the unit circle. Therefore the approximate point spectrum of T is its entire spectrum. This is true for a more general class of operators.

A unitary operator is normal. By spectral theorem, a bounded operator on a Hilbert space is normal if and only if it is a multiplication operator. It can be shown that, in general, the approximate point spectrum of a bounded multiplication operator is its spectrum.

Residual spectrum

An operator may be injective, even bounded below, but not invertible. The unilateral shift on l 2(N) is such an example. This shift operator is an isometry, therefore bounded below by 1. But it is not invertible as it is not surjective. The set of λ for which λI - T is injective but does not have dense range is known as the residual spectrum or compression spectrum of T and is denoted by σr(T).

Continuous spectrum

The set of all λ for which λI - T is injective and has dense range, but is not surjective, is called the continuous spectrum of T, denoted by σc(T) . The continuous spectrum therefore consists of those approximate eigenvalues which are not eigenvalues and do not lie in the residual spectrum. That is,

\sigma_c(T) = (\sigma_{ap}(T) \setminus \sigma_r(T)) \cup (\sigma_{ap}(T) \setminus \sigma_p(T)) .

Peripheral spectrum

The peripheral spectrum of an operator is defined as the set of points in its spectrum which have modulus equal to its spectral radius.

Further results

If T is a compact operator, then it can be shown that any nonzero λ in the spectrum is an eigenvalue. In other words, the spectrum of such an operator, which was defined as a generalization of the concept of eigenvalues, consists in this case only of the usual eigenvalues, and possibly 0.

If X is a Hilbert space and T is a normal operator, then a remarkable result known as the spectral theorem gives an analogue of the diagonalisation theorem for normal finite-dimensional operators (Hermitian matrices, for example).

Spectrum of an unbounded operator

One can extend the definition of spectrum for unbounded operators on a Banach space X, operators which are no longer elements in the Banach algebra B(X). One proceeds in a manner similar to the bounded case. A complex number λ is said to be in the resolvent set, that is, the complement of the spectrum of a linear operator

T: D \subset X \to X

if the operator

T-\lambda I: D \to X

has a bounded inverse, i.e. if there exists a bounded operator

S : X \rightarrow D

such that

S (T - \lambda) = I_D, \,  (T - \lambda) S  = I_X.

A complex number λ is then in the spectrum if this property fails to hold. One can classify the spectrum in exactly the same way as in the bounded case.

The spectrum of an unbounded operator is in general a closed, possibly empty, subset of the complex plane.

For λ to be in the resolvent (i.e. not in the spectrum), as in the bounded case λI − T must be bijective, since it must have a two-sided inverse. As before if an inverse exists then its linearity is immediate, but in general it may not be bounded, so this condition must be checked separately.

However, boundedness of the inverse does follow directly from its existence if one introduces the additional assumption that T is closed; this follows from the closed graph theorem. Therefore, as in the bounded case, a complex number λ lies in the spectrum of a closed operator T if and only if λI − T is not bijective. Note that the class of closed operators includes all bounded operators.

Spectrum of a unital Banach algebra

Let B be a complex Banach algebra containing a unit e. Then we define the spectrum σ(x) (or more explicitly σB(x)) of an element x of B to be the set of those complex numbers λ for which λe − x is not invertible in B. This extends the definition for bounded linear operators B(X) on a Banach space X, since B(X) is a Banach algebra.

See also


  1. ^ Theorem 3.3.3 of Kadison & Ringrose, 1983, Fundamentals of the Theory of Operator Algebras, Vol. I: Elementary Theory, New York: Academic Press, Inc.
  • Dales et al., Introduction to Banach Algebras, Operators, and Harmonic Analysis, ISBN 0-521-53584-0

Wikimedia Foundation. 2010.

Игры ⚽ Нужно решить контрольную?

Look at other dictionaries:

  • Decomposition of spectrum (functional analysis) — In mathematics, especially functional analysis, the spectrum of an operator generalizes the notion of eigenvalues. Given an operator, it is sometimes useful to break up the spectrum into various parts. This article discusses a few examples of… …   Wikipedia

  • List of functional analysis topics — This is a list of functional analysis topics, by Wikipedia page. Contents 1 Hilbert space 2 Functional analysis, classic results 3 Operator theory 4 Banach space examples …   Wikipedia

  • analysis — /euh nal euh sis/, n., pl. analyses / seez /. 1. the separating of any material or abstract entity into its constituent elements (opposed to synthesis). 2. this process as a method of studying the nature of something or of determining its… …   Universalium

  • Spectrum (disambiguation) — A spectrum is a condition or value that is not limited to a specific set of values but can vary infinitely within a continuum.Spectrum may refer to:In physical sciences: * electromagnetic spectrum ** visible spectrum or optical spectrum, a subset …   Wikipedia

  • Functional magnetic resonance imaging — Functional MRI or functional Magnetic Resonance Imaging (fMRI) is a type of specialized MRI scan. It measures the haemodynamic response related to neural activity in the brain or spinal cord of humans or other animals. It is one of the most… …   Wikipedia

  • Discrete spectrum — For a mathematically rigorous point of view of discrete spectrum , see decomposition of spectrum (functional analysis). In physics, an elementary (and accurate) explanation of a discrete spectrum is that it is an emission spectrum or absorption… …   Wikipedia

  • Continuous spectrum — The spectrum of a linear operator is commonly divided into three parts: point spectrum, continuous spectrum, and residual spectrum. If H is a topological vector space and is a linear map, the spectrum of A is the set of complex numbers λ such… …   Wikipedia

  • Professional practice of behavior analysis — The professional practice of behavior analysis is the fourth domain of behavior analysis. The other three are behaviorism, experimental analysis of behavior, and applied behavior analysis. [Cooper, et al. p. 20] The professional practice of… …   Wikipedia

  • Applied behavior analysis — (ABA) is the science of applying experimentally derived principles of behavior to improve socially significant behavior. ABA takes what we know about behavior and uses it to bring about positive change (Applied). Behaviors are defined in… …   Wikipedia

  • List of numerical analysis topics — This is a list of numerical analysis topics, by Wikipedia page. Contents 1 General 2 Error 3 Elementary and special functions 4 Numerical linear algebra …   Wikipedia

Share the article and excerpts

Direct link
Do a right-click on the link above
and select “Copy Link”