Spectral theorem

Spectral theorem In mathematics, particularly linear algebra and functional analysis, a spectral theorem is a result about when a linear operator or matrix can be diagonalized (that is, represented as a diagonal matrix in some basis). This is extremely useful because computations involving a diagonalizable matrix can often be reduced to much simpler computations involving the corresponding diagonal matrix. The concept of diagonalization is relatively straightforward for operators on finite-dimensional vector spaces but requires some modification for operators on infinite-dimensional spaces. In general, the spectral theorem identifies a class of linear operators that can be modeled by multiplication operators, which are as simple as one can hope to find. In more abstract language, the spectral theorem is a statement about commutative C*-algebras. See also spectral theory for a historical perspective.

Examples of operators to which the spectral theorem applies are self-adjoint operators or more generally normal operators on Hilbert spaces.

The spectral theorem also provides a canonical decomposition, called the spectral decomposition, of the underlying vector space on which the operator acts.

Augustin-Louis Cauchy proved the spectral theorem for symmetric matrices, i.e., that every real, symmetric matrix is diagonalizable. In addition, Cauchy was the first to be systematic about determinants.[1][2] The spectral theorem as generalized by John von Neumann is today perhaps the most important result of operator theory.

This article mainly focuses on the simplest kind of spectral theorem, that for a self-adjoint operator on a Hilbert space. However, as noted above, the spectral theorem also holds for normal operators on a Hilbert space.

Contents 1 Finite-dimensional case 1.1 Hermitian maps and Hermitian matrices 1.2 Normal matrices 2 Compact self-adjoint operators 3 Bounded self-adjoint operators 3.1 Possible absence of eigenvectors 3.2 Spectral subspaces and projection-valued measures 3.3 Multiplication operator version 3.4 Direct integrals 3.5 Cyclic vectors and simple spectrum 3.6 Functional calculus 4 General self-adjoint operators 5 See also 6 Notes 7 References Finite-dimensional case Hermitian maps and Hermitian matrices We begin by considering a Hermitian matrix on {displaystyle mathbb {C} ^{n}} (but the following discussion will be adaptable to the more restrictive case of symmetric matrices on {displaystyle mathbb {R} ^{n}} ). We consider a Hermitian map A on a finite-dimensional complex inner product space V endowed with a positive definite sesquilinear inner product {displaystyle langle cdot ,cdot rangle } . The Hermitian condition on {displaystyle A} means that for all x, y ∈ V, {displaystyle langle Ax,yrangle =langle x,Ayrangle .} An equivalent condition is that A* = A, where A* is the Hermitian conjugate of A. In the case that A is identified with a Hermitian matrix, the matrix of A* can be identified with its conjugate transpose. (If A is a real matrix, then this is equivalent to AT = A, that is, A is a symmetric matrix.) This condition implies that all eigenvalues of a Hermitian map are real: it is enough to apply it to the case when x = y is an eigenvector. (Recall that an eigenvector of a linear map A is a (non-zero) vector x such that Ax = λx for some scalar λ. The value λ is the corresponding eigenvalue. Moreover, the eigenvalues are roots of the characteristic polynomial.) Theorem. If A is Hermitian, then there exists an orthonormal basis of V consisting of eigenvectors of A. Each eigenvalue is real.

We provide a sketch of a proof for the case where the underlying field of scalars is the complex numbers.

By the fundamental theorem of algebra, applied to the characteristic polynomial of A, there is at least one eigenvalue λ1 and eigenvector e1. Then since {displaystyle lambda _{1}langle e_{1},e_{1}rangle =langle A(e_{1}),e_{1}rangle =langle e_{1},A(e_{1})rangle ={bar {lambda }}_{1}langle e_{1},e_{1}rangle ,} we find that λ1 is real. Now consider the space K = span{e1}⊥, the orthogonal complement of e1. By Hermiticity, K is an invariant subspace of A. Applying the same argument to K shows that A has an eigenvector e2 ∈ K. Finite induction then finishes the proof.

The spectral theorem holds also for symmetric maps on finite-dimensional real inner product spaces, but the existence of an eigenvector does not follow immediately from the fundamental theorem of algebra. To prove this, consider A as a Hermitian matrix and use the fact that all eigenvalues of a Hermitian matrix are real.

The matrix representation of A in a basis of eigenvectors is diagonal, and by the construction the proof gives a basis of mutually orthogonal eigenvectors; by choosing them to be unit vectors one obtains an orthonormal basis of eigenvectors. A can be written as a linear combination of pairwise orthogonal projections, called its spectral decomposition. Let {displaystyle V_{lambda }={vin V_Av=lambda v}} be the eigenspace corresponding to an eigenvalue λ. Note that the definition does not depend on any choice of specific eigenvectors. V is the orthogonal direct sum of the spaces Vλ where the index ranges over eigenvalues.

In other words, if Pλ denotes the orthogonal projection onto Vλ, and λ1, ..., λm are the eigenvalues of A, then the spectral decomposition may be written as {displaystyle A=lambda _{1}P_{lambda _{1}}+cdots +lambda _{m}P_{lambda _{m}}.} If the spectral decomposition of A is {displaystyle A=lambda _{1}P_{1}+cdots +lambda _{m}P_{m}} , then {displaystyle A^{2}=(lambda _{1})^{2}P_{1}+cdots +(lambda _{m})^{2}P_{m}} and {displaystyle mu A=mu lambda _{1}P_{1}+cdots +mu lambda _{m}P_{m}} for any scalar {displaystyle mu .} It follows that for any polynomial f one has {displaystyle f(A)=f(lambda _{1})P_{1}+cdots +f(lambda _{m})P_{m}.} The spectral decomposition is a special case of both the Schur decomposition and the singular value decomposition.

Normal matrices Main article: Normal matrix The spectral theorem extends to a more general class of matrices. Let A be an operator on a finite-dimensional inner product space. A is said to be normal if A*A = AA*. One can show that A is normal if and only if it is unitarily diagonalizable. Proof: By the Schur decomposition, we can write any matrix as A = UTU*, where U is unitary and T is upper-triangular. If A is normal, then one sees that TT* = T*T. Therefore, T must be diagonal since a normal upper triangular matrix is diagonal (see normal matrix). The converse is obvious.

In other words, A is normal if and only if there exists a unitary matrix U such that {displaystyle A=UDU^{*},} where D is a diagonal matrix. Then, the entries of the diagonal of D are the eigenvalues of A. The column vectors of U are the eigenvectors of A and they are orthonormal. Unlike the Hermitian case, the entries of D need not be real.

Compact self-adjoint operators Main article: Compact operator on Hilbert space In the more general setting of Hilbert spaces, which may have an infinite dimension, the statement of the spectral theorem for compact self-adjoint operators is virtually the same as in the finite-dimensional case.

Theorem. Suppose A is a compact self-adjoint operator on a (real or complex) Hilbert space V. Then there is an orthonormal basis of V consisting of eigenvectors of A. Each eigenvalue is real.

As for Hermitian matrices, the key point is to prove the existence of at least one nonzero eigenvector. One cannot rely on determinants to show existence of eigenvalues, but one can use a maximization argument analogous to the variational characterization of eigenvalues.

If the compactness assumption is removed, then it is not true that every self-adjoint operator has eigenvectors.

Bounded self-adjoint operators See also: Eigenfunction and Self-adjoint operator § Spectral theorem Possible absence of eigenvectors The next generalization we consider is that of bounded self-adjoint operators on a Hilbert space. Such operators may have no eigenvalues: for instance let A be the operator of multiplication by t on {displaystyle L^{2}([0,1])} , that is,[3] {displaystyle [Avarphi ](t)=tvarphi (t).;} This operator does not have any eigenvectors in {displaystyle L^{2}([0,1])} , though it does have eigenvectors in a larger space. Namely the distribution {displaystyle varphi (t)=delta (t-t_{0})} , where {displaystyle delta } is the Dirac delta function, is an eigenvector when construed in an appropriate sense. The Dirac delta function is however not a function in the classical sense and does not lie in the Hilbert space L2[0, 1] or any other Banach space. Thus, the delta-functions are "generalized eigenvectors" of {displaystyle A} but not eigenvectors in the usual sense.

Spectral subspaces and projection-valued measures In the absence of (true) eigenvectors, one can look for subspaces consisting of almost eigenvectors. In the above example, for example, where {displaystyle [Avarphi ](t)=tvarphi (t),;} we might consider the subspace of functions supported on a small interval {displaystyle [a,a+varepsilon ]} inside {displaystyle [0,1]} . This space is invariant under {displaystyle A} and for any {displaystyle varphi } in this subspace, {displaystyle Avarphi } is very close to {displaystyle avarphi } . In this approach to the spectral theorem, if {displaystyle A} is a bounded self-adjoint operator, then one looks for large families of such "spectral subspaces".[4] Each subspace, in turn, is encoded by the associated projection operator, and the collection of all the subspaces is then represented by a projection-valued measure.

One formulation of the spectral theorem expresses the operator A as an integral of the coordinate function over the operator's spectrum {displaystyle sigma (A)} with respect to a projection-valued measure.[5] {displaystyle A=int _{sigma (A)}lambda ,dE_{lambda }.} When the self-adjoint operator in question is compact, this version of the spectral theorem reduces to something similar to the finite-dimensional spectral theorem above, except that the operator is expressed as a finite or countably infinite linear combination of projections, that is, the measure consists only of atoms.

Multiplication operator version An alternative formulation of the spectral theorem says that every bounded self-adjoint operator is unitarily equivalent to a multiplication operator. The significance of this result is that multiplication operators are in many ways easy to understand.

Theorem.[6] — Let A be a bounded self-adjoint operator on a Hilbert space H. Then there is a measure space (X, Σ, μ) and a real-valued essentially bounded measurable function f on X and a unitary operator U:H → L2μ(X) such that {displaystyle U^{*}TU=A,} where T is the multiplication operator: {displaystyle [Tvarphi ](x)=f(x)varphi (x),} and {displaystyle |T|=|f|_{infty }} .

The spectral theorem is the beginning of the vast research area of functional analysis called operator theory; see also the spectral measure.

There is also an analogous spectral theorem for bounded normal operators on Hilbert spaces. The only difference in the conclusion is that now f may be complex-valued.

Direct integrals There is also a formulation of the spectral theorem in terms of direct integrals. It is similar to the multiplication-operator formulation, but more canonical.

Let {displaystyle A} be a bounded self-adjoint operator and let {displaystyle sigma (A)} be the spectrum of {displaystyle A} . The direct-integral formulation of the spectral theorem associates two quantities to {displaystyle A} . First, a measure {displaystyle mu } on {displaystyle sigma (A)} , and second, a family of Hilbert spaces {displaystyle {H_{lambda }},,,lambda in sigma (A).} We then form the direct integral Hilbert space {displaystyle int _{mathbf {R} }^{oplus }H_{lambda },dmu (lambda ).} The elements of this space are functions (or "sections") {displaystyle s(lambda ),,,lambda in sigma (A),} such that {displaystyle s(lambda )in H_{lambda }} for all {displaystyle lambda } . The direct-integral version of the spectral theorem may be expressed as follows:[7] Theorem — If {displaystyle A} is a bounded self-adjoint operator, then {displaystyle A} is unitarily equivalent to the "multiplication by {displaystyle lambda } " operator on {displaystyle int _{mathbf {R} }^{oplus }H_{lambda },dmu (lambda )} for some measure {displaystyle mu } and some family {displaystyle {H_{lambda }}} of Hilbert spaces. The measure {displaystyle mu } is uniquely determined by {displaystyle A} up to measure-theoretic equivalence; that is, any two measure associated to the same {displaystyle A} have the same sets of measure zero. The dimensions of the Hilbert spaces {displaystyle H_{lambda }} are uniquely determined by {displaystyle A} up to a set of {displaystyle mu } -measure zero.

The spaces {displaystyle H_{lambda }} can be thought of as something like "eigenspaces" for {displaystyle A} . Note, however, that unless the one-element set {displaystyle {lambda }} has positive measure, the space {displaystyle H_{lambda }} is not actually a subspace of the direct integral. Thus, the {displaystyle H_{lambda }} 's should be thought of as "generalized eigenspace"—that is, the elements of {displaystyle H_{lambda }} are "eigenvectors" that do not actually belong to the Hilbert space.

Although both the multiplication-operator and direct integral formulations of the spectral theorem express a self-adjoint operator as unitarily equivalent to a multiplication operator, the direct integral approach is more canonical. First, the set over which the direct integral takes place (the spectrum of the operator) is canonical. Second, the function we are multiplying by is canonical in the direct-integral approach: Simply the function {displaystyle lambda mapsto lambda } .

Cyclic vectors and simple spectrum A vector {displaystyle varphi } is called a cyclic vector for {displaystyle A} if the vectors {displaystyle varphi ,Avarphi ,A^{2}varphi ,ldots } span a dense subspace of the Hilbert space. Suppose {displaystyle A} is a bounded self-adjoint operator for which a cyclic vector exists. In that case, there is no distinction between the direct-integral and multiplication-operator formulations of the spectral theorem. Indeed, in that case, there is a measure {displaystyle mu } on the spectrum {displaystyle sigma (A)} of {displaystyle A} such that {displaystyle A} is unitarily equivalent to the "multiplication by {displaystyle lambda } " operator on {displaystyle L^{2}(sigma (A),mu )} .[8] This result represents {displaystyle A} simultaneously as a multiplication operator and as a direct integral, since {displaystyle L^{2}(sigma (A),mu )} is just a direct integral in which each Hilbert space {displaystyle H_{lambda }} is just {displaystyle mathbb {C} } .

Not every bounded self-adjoint operator admits a cyclic vector; indeed, by the uniqueness in the direct integral decomposition, this can occur only when all the {displaystyle H_{lambda }} 's have dimension one. When this happens, we say that {displaystyle A} has "simple spectrum" in the sense of spectral multiplicity theory. That is, a bounded self-adjoint operator that admits a cyclic vector should be thought of as the infinite-dimensional generalization of a self-adjoint matrix with distinct eigenvalues (i.e., each eigenvalue has multiplicity one).

Although not every {displaystyle A} admits a cyclic vector, it is easy to see that we can decompose the Hilbert space as a direct sum of invariant subspaces on which {displaystyle A} has a cyclic vector. This observation is the key to the proofs of the multiplication-operator and direct-integral forms of the spectral theorem.

Functional calculus One important application of the spectral theorem (in whatever form) is the idea of defining a functional calculus. That is, given a function {displaystyle f} defined on the spectrum of {displaystyle A} , we wish to define an operator {displaystyle f(A)} . If {displaystyle f} is simply a positive power, {displaystyle f(x)=x^{n}} , then {displaystyle f(A)} is just the {displaystyle nmathrm {th} } power of {displaystyle A} , {displaystyle A^{n}} . The interesting cases are where {displaystyle f} is a nonpolynomial function such as a square root or an exponential. Either of the versions of the spectral theorem provides such a functional calculus.[9] In the direct-integral version, for example, {displaystyle f(A)} acts as the "multiplication by {displaystyle f} " operator in the direct integral: {displaystyle [f(A)s](lambda )=f(lambda )s(lambda )} .

That is to say, each space {displaystyle H_{lambda }} in the direct integral is a (generalized) eigenspace for {displaystyle f(A)} with eigenvalue {displaystyle f(lambda )} .

General self-adjoint operators Many important linear operators which occur in analysis, such as differential operators, are unbounded. There is also a spectral theorem for self-adjoint operators that applies in these cases. To give an example, every constant-coefficient differential operator is unitarily equivalent to a multiplication operator. Indeed, the unitary operator that implements this equivalence is the Fourier transform; the multiplication operator is a type of Fourier multiplier.

In general, spectral theorem for self-adjoint operators may take several equivalent forms.[10] Notably, all of the formulations given in the previous section for bounded self-adjoint operators—the projection-valued measure version, the multiplication-operator version, and the direct-integral version—continue to hold for unbounded self-adjoint operators, with small technical modifications to deal with domain issues.

See also Hahn-Hellinger theorem Spectral theory of compact operators Spectral theory of normal C*-algebras Borel functional calculus Spectral theory Matrix decomposition Canonical form Jordan decomposition, of which the spectral decomposition is a special case. Singular value decomposition, a generalisation of spectral theorem to arbitrary matrices. Eigendecomposition of a matrix Wiener–Khinchin theorem Notes ^ Hawkins, Thomas (1975). "Cauchy and the spectral theory of matrices". Historia Mathematica. 2: 1–29. doi:10.1016/0315-0860(75)90032-4. ^ A Short History of Operator Theory by Evans M. Harrell II ^ Hall 2013 Section 6.1 ^ Hall 2013 Theorem 7.2.1 ^ Hall 2013 Theorem 7.12 ^ Hall 2013 Theorem 7.20 ^ Hall 2013 Theorem 7.19 ^ Hall 2013 Lemma 8.11 ^ E.g., Hall 2013 Definition 7.13 ^ See Section 10.1 of Hall 2013 References Sheldon Axler, Linear Algebra Done Right, Springer Verlag, 1997 Hall, B.C. (2013), Quantum Theory for Mathematicians, Graduate Texts in Mathematics, vol. 267, Springer, ISBN 978-1461471158 Paul Halmos, "What Does the Spectral Theorem Say?", American Mathematical Monthly, volume 70, number 3 (1963), pages 241–247 Other link M. Reed and B. Simon, Methods of Mathematical Physics, vols I–IV, Academic Press 1972. G. Teschl, Mathematical Methods in Quantum Mechanics with Applications to Schrödinger Operators, https://www.mat.univie.ac.at/~gerald/ftp/book-schroe/, American Mathematical Society, 2009. Valter Moretti (2018). Spectral Theory and Quantum Mechanics; Mathematical Foundations of Quantum Theories, Symmetries and Introduction to the Algebraic Formulation 2nd Edition. Springer. ISBN 978-3-319-70705-1. show vte Functional analysis (topics – glossary) show vte Spectral theory and *-algebras Categories: Spectral theoryLinear algebraMatrix theorySingular value decompositionTheorems in functional analysisTheorems in linear algebra

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