Compact operator on Hilbert space

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In the mathematical discipline of functional analysis, the concept of a compact operator on Hilbert space is an extension of the concept of a matrix acting on a finite-dimensional vector space; in Hilbert space, compact operators are precisely the closure of finite-rank operators (representable by finite-dimensional matrices) in the topology induced by the operator norm. As such, results from matrix theory can sometimes be extended to compact operators using similar arguments. By contrast, the study of general operators on infinite-dimensional spaces often requires a genuinely different approach.

For example, the spectral theory of compact operators on Banach spaces takes a form that is very similar to the Jordan canonical form of matrices. In the context of Hilbert spaces, a square matrix is unitarily diagonalizable if and only if it is normal. A corresponding result holds for normal compact operators on Hilbert spaces. More generally, the compactness assumption can be dropped. As stated above, the techniques used to prove results, e.g., the spectral theorem, in the non-compact case are typically different, involving operator-valued measures on the spectrum.

Some results for compact operators on Hilbert space will be discussed, starting with general properties before considering subclasses of compact operators.

Let ${\displaystyle H}$ be a Hilbert space and ${\displaystyle L(H)}$ be the set of bounded operators on ${\displaystyle H}$. Then, an operator ${\displaystyle T\in L(H)}$ is said to be a compact operator if the image of each bounded set under ${\displaystyle T}$ is relatively compact.

We list in this section some general properties of compact operators.

If X and Y are separable Hilbert spaces (in fact, X Banach and Y normed will suffice), then T : X → Y is compact if and only if it is sequentially continuous when viewed as a map from X with the weak topology to Y (with the norm topology). (See (Zhu 2007, Theorem 1.14, p.11), and note in this reference that the uniform boundedness will apply in the situation where F ⊆ X satisfies (∀φ ∈ Hom(X, K)) sup{x**(φ) = φ(x) : x} < ∞, where K is the underlying field. The uniform boundedness principle applies since Hom(X, K) with the norm topology will be a Banach space, and the maps x** : Hom(X,K) → K are continuous homomorphisms with respect to this topology.)

The family of compact operators is a norm-closed, two-sided, *-ideal in L(H). Consequently, a compact operator T cannot have a bounded inverse if H is infinite-dimensional. If ST = TS = I, then the identity operator would be compact, a contradiction.

If sequences of bounded operators B_n → B, C_n → C in the strong operator topology and T is compact, then ${\displaystyle B_{n}TC_{n}^{*}}$ converges to ${\displaystyle BTC^{*}}$ in norm.^[1] For example, consider the Hilbert space ${\displaystyle \ell ^{2}(\mathbf {N} ),}$ with standard basis {e_n}. Let P_m be the orthogonal projection on the linear span of {e₁, ..., e_m}. The sequence {P_m} converges to the identity operator I strongly but not uniformly. Define T by ${\displaystyle Te_{n}={\tfrac {1}{n^{2}}}e_{n}.}$ T is compact, and, as claimed above, P_mT → IT = T in the uniform operator topology: for all x, $${\displaystyle \left\|P_{m}Tx-Tx\right\|\leq \left({\frac {1}{m+1}}\right)^{2}\|x\|.}$$

Notice each P_m is a finite-rank operator. Similar reasoning shows that if T is compact, then T is the uniform limit of some sequence of finite-rank operators.

By the norm-closedness of the ideal of compact operators, the converse is also true.

The quotient C*-algebra of L(H) modulo the compact operators is called the Calkin algebra, in which one can consider properties of an operator up to compact perturbation.

A bounded operator T on a Hilbert space H is said to be self-adjoint if T = T*, or equivalently,

$${\displaystyle \langle Tx,y\rangle =\langle x,Ty\rangle ,\quad x,y\in H.}$$

It follows that ⟨Tx, x⟩ is real for every x ∈ H, thus eigenvalues of T, when they exist, are real. When a closed linear subspace L of H is invariant under T, then the restriction of T to L is a self-adjoint operator on L, and furthermore, the orthogonal complement L^⊥ of L is also invariant under T. For example, the space H can be decomposed as the orthogonal direct sum of two T–invariant closed linear subspaces: the kernel of T, and the orthogonal complement (ker T)^⊥ of the kernel (which is equal to the closure of the range of T, for any bounded self-adjoint operator). These basic facts play an important role in the proof of the spectral theorem below.

The classification result for Hermitian n × n matrices is the spectral theorem: If M = M*, then M is unitarily diagonalizable, and the diagonalization of M has real entries. Let T be a compact self-adjoint operator on a Hilbert space H. We will prove the same statement for T: the operator T can be diagonalized by an orthonormal set of eigenvectors, each of which corresponds to a real eigenvalue.

Theorem For every compact self-adjoint operator T on a real or complex Hilbert space H, there exists an orthonormal basis of H consisting of eigenvectors of T. More specifically, the orthogonal complement of the kernel of T admits either a finite orthonormal basis of eigenvectors of T, or a countably infinite orthonormal basis {e_n} of eigenvectors of T, with corresponding eigenvalues {λ_n} ⊂ R, such that λ_n → 0.

In other words, a compact self-adjoint operator can be unitarily diagonalized. This is the spectral theorem.

When H is separable, one can mix the basis {e_n} with a countable orthonormal basis for the kernel of T, and obtain an orthonormal basis {f_n} for H, consisting of eigenvectors of T with real eigenvalues {μ_n} such that μ_n → 0.

Corollary For every compact self-adjoint operator T on a real or complex separable infinite-dimensional Hilbert space H, there exists a countably infinite orthonormal basis {f_n} of H consisting of eigenvectors of T, with corresponding eigenvalues {μ_n} ⊂ R, such that μ_n → 0.

Let us discuss first the finite-dimensional proof. Proving the spectral theorem for a Hermitian n × n matrix T hinges on showing the existence of one eigenvector x. Once this is done, Hermiticity implies that both the linear span and orthogonal complement of x (of dimension n − 1) are invariant subspaces of T. The desired result is then obtained by induction for ${\displaystyle T_{x^{\perp }}}$.

The existence of an eigenvector can be shown in (at least) two alternative ways:

1. One can argue algebraically: The characteristic polynomial of T has a complex root, therefore T has an eigenvalue with a corresponding eigenvector.
2. The eigenvalues can be characterized variationally: The largest eigenvalue is the maximum on the closed unit sphere of the function f: R²ⁿ → R defined by f(x) = x*Tx = ⟨Tx, x⟩.

Note. In the finite-dimensional case, part of the first approach works in much greater generality; any square matrix, not necessarily Hermitian, has an eigenvector. This is simply not true for general operators on Hilbert spaces. In infinite dimensions, it is also not immediate how to generalize the concept of the characteristic polynomial.

The spectral theorem for the compact self-adjoint case can be obtained analogously: one finds an eigenvector by extending the second finite-dimensional argument above, then apply induction. We first sketch the argument for matrices.

Since the closed unit sphere S in R²ⁿ is compact, and f is continuous, f(S) is compact on the real line, therefore f attains a maximum on S, at some unit vector y. By Lagrange's multiplier theorem, y satisfies $${\displaystyle \nabla f=\nabla y^{*}Ty=\lambda \cdot \nabla y^{*}y}$$ for some λ. By Hermiticity, Ty = λy.

Alternatively, let z ∈ Cⁿ be any vector. Notice that if a unit vector y maximizes ⟨Tx, x⟩ on the unit sphere (or on the unit ball), it also maximizes the Rayleigh quotient: $${\displaystyle g(x)={\frac {\langle Tx,x\rangle }{\|x\|^{2}}},\qquad 0\neq x\in \mathbf {C} ^{n}.}$$

Consider the function: $${\displaystyle {\begin{cases}h:\mathbf {R} \to \mathbf {R} \\h(t)=g(y+tz)\end{cases}}}$$

By calculus, h′(0) = 0, i.e., $${\displaystyle {\begin{aligned}h'(0)&=\lim _{t\to 0}{\frac {h(t)-h(0)}{t-0}}\\&=\lim _{t\to 0}{\frac {g(y+tz)-g(y)}{t}}\\&=\lim _{t\to 0}{\frac {1}{t}}\left({\frac {\langle T(y+tz),y+tz\rangle }{\|y+tz\|^{2}}}-{\frac {\langle Ty,y\rangle }{\|y\|^{2}}}\right)\\&=\lim _{t\to 0}{\frac {1}{t}}\left({\frac {\langle T(y+tz),y+tz\rangle -\langle Ty,y\rangle }{\|y\|^{2}}}\right)\\&={\frac {1}{\|y\|^{2}}}\lim _{t\to 0}{\frac {\langle T(y+tz),y+tz\rangle -\langle Ty,y\rangle }{t}}\\&={\frac {1}{\|y\|^{2}}}\left({\frac {d}{dt}}{\frac {\langle T(y+tz),y+tz\rangle }{\langle y+tz,y+tz\rangle }}\right)(0)\\&=0.\end{aligned}}}$$

Define: $${\displaystyle m={\frac {\langle Ty,y\rangle }{\langle y,y\rangle }}}$$

After some algebra the above expression becomes (Re denotes the real part of a complex number) $${\displaystyle \operatorname {Re} (\langle Ty-my,z\rangle )=0.}$$

But z is arbitrary, therefore Ty − my = 0. This is the crux of proof for spectral theorem in the matricial case.

Note that while the Lagrange multipliers generalize to the infinite-dimensional case, the compactness of the unit sphere is lost. This is where the assumption that the operator T be compact is useful.

Claim  If T is a compact self-adjoint operator on a non-zero Hilbert space H and $${\displaystyle m(T):=\sup {\bigl \{}|\langle Tx,x\rangle |:x\in H,\,\|x\|\leq 1{\bigr \}},}$$ then m(T) or −m(T) is an eigenvalue of T.

If m(T) = 0, then T = 0 by the polarization identity, and this case is clear. Consider the function $${\displaystyle {\begin{cases}f:H\to \mathbf {R} \\f(x)=\langle Tx,x\rangle \end{cases}}}$$

Replacing T by −T if necessary, one may assume that the supremum of f on the closed unit ball B ⊂ H is equal to m(T) > 0. If f attains its maximum m(T) on B at some unit vector y, then, by the same argument used for matrices, y is an eigenvector of T, with corresponding eigenvalue λ = ⟨λy, y⟩ = ⟨Ty, y⟩ = f(y) = m(T).

By the Banach–Alaoglu theorem and the reflexivity of H, the closed unit ball B is weakly compact. Also, the compactness of T means (see above) that T : X with the weak topology → X with the norm topology is continuous ^{[disputed – discuss]}. These two facts imply that f is continuous on B equipped with the weak topology, and f attains therefore its maximum m on B at some y ∈ B. By maximality, ${\displaystyle \|y\|=1,}$ which in turn implies that y also maximizes the Rayleigh quotient g(x) (see above). This shows that y is an eigenvector of T, and ends the proof of the claim.

Note. The compactness of T is crucial. In general, f need not be continuous for the weak topology on the unit ball B. For example, let T be the identity operator, which is not compact when H is infinite-dimensional. Take any orthonormal sequence {y_n}. Then y_n converges to 0 weakly, but lim f(y_n) = 1 ≠ 0 = f(0).

Let T be a compact operator on a Hilbert space H. A finite (possibly empty) or countably infinite orthonormal sequence {e_n} of eigenvectors of T, with corresponding non-zero eigenvalues, is constructed by induction as follows. Let H₀ = H and T₀ = T. If m(T₀) = 0, then T = 0 and the construction stops without producing any eigenvector e_n. Suppose that orthonormal eigenvectors e₀, ..., e_{n − 1} of T have been found. Then E_n := span(e₀, ..., e_{n − 1}) is invariant under T, and by self-adjointness, the orthogonal complement H_n of E_n is an invariant subspace of T. Let T_n denote the restriction of T to H_n. If m(T_n) = 0, then T_n = 0, and the construction stops. Otherwise, by the claim applied to T_n, there is a norm one eigenvector e_n of T in H_n, with corresponding non-zero eigenvalue λ_n = ± m(T_n).

Let F = (span{e_n})^⊥, where {e_n} is the finite or infinite sequence constructed by the inductive process; by self-adjointness, F is invariant under T. Let S denote the restriction of T to F. If the process was stopped after finitely many steps, with the last vector e_m−1, then F= H_m and S = T_m = 0 by construction. In the infinite case, compactness of T and the weak-convergence of e_n to 0 imply that Te_n = λ_ne_n → 0, therefore λ_n → 0. Since F is contained in H_n for every n, it follows that m(S) ≤ m({T_n}) = |λ_n| for every n, hence m(S) = 0. This implies again that S = 0.

The fact that S = 0 means that F is contained in the kernel of T. Conversely, if x ∈ ker(T) then by self-adjointness, x is orthogonal to every eigenvector {e_n} with non-zero eigenvalue. It follows that F = ker(T), and that {e_n} is an orthonormal basis for the orthogonal complement of the kernel of T. One can complete the diagonalization of T by selecting an orthonormal basis of the kernel. This proves the spectral theorem.

A shorter but more abstract proof goes as follows: by Zorn's lemma, select U to be a maximal subset of H with the following three properties: all elements of U are eigenvectors of T, they have norm one, and any two distinct elements of U are orthogonal. Let F be the orthogonal complement of the linear span of U. If F ≠ {0}, it is a non-trivial invariant subspace of T, and by the initial claim, there must exist a norm one eigenvector y of T in F. But then U ∪ {y} contradicts the maximality of U. It follows that F = {0}, hence span(U) is dense in H. This shows that U is an orthonormal basis of H consisting of eigenvectors of T.

If T is compact on an infinite-dimensional Hilbert space H, then T is not invertible, hence σ(T), the spectrum of T, always contains 0. The spectral theorem shows that σ(T) consists of the eigenvalues {λ_n} of T and of 0 (if 0 is not already an eigenvalue). The set σ(T) is a compact subset of the complex numbers, and the eigenvalues are dense in σ(T).

Any spectral theorem can be reformulated in terms of a functional calculus. In the present context, we have:

Theorem. Let C(σ(T)) denote the C*-algebra of continuous functions on σ(T). There exists a unique isometric homomorphism Φ : C(σ(T)) → L(H) such that Φ(1) = I and, if f is the identity function f(λ) = λ, then Φ(f) = T. Moreover, σ(f(T)) = f(σ(T)).

The functional calculus map Φ is defined in a natural way: let {e_n} be an orthonormal basis of eigenvectors for H, with corresponding eigenvalues {λ_n}; for f ∈ C(σ(T)), the operator Φ(f), diagonal with respect to the orthonormal basis {e_n}, is defined by setting $${\displaystyle \Phi (f)(e_{n})=f(\lambda _{n})e_{n}}$$ for every n. Since Φ(f) is diagonal with respect to an orthonormal basis, its norm is equal to the supremum of the modulus of diagonal coefficients, $${\displaystyle \|\Phi (f)\|=\sup _{\lambda _{n}\in \sigma (T)}|f(\lambda _{n})|=\|f\|_{C(\sigma (T))}.}$$

The other properties of Φ can be readily verified. Conversely, any homomorphism Ψ satisfying the requirements of the theorem must coincide with Φ when f is a polynomial. By the Weierstrass approximation theorem, polynomial functions are dense in C(σ(T)), and it follows that Ψ = Φ. This shows that Φ is unique.

The more general continuous functional calculus can be defined for any self-adjoint (or even normal, in the complex case) bounded linear operator on a Hilbert space. The compact case described here is a particularly simple instance of this functional calculus.

Consider an Hilbert space H (e.g. the finite-dimensional Cⁿ), and a commuting set ${\displaystyle {\mathcal {F}}\subseteq \operatorname {Hom} (H,H)}$ of self-adjoint operators. Then under suitable conditions, it can be simultaneously (unitarily) diagonalized. Viz., there exists an orthonormal basis Q consisting of common eigenvectors for the operators — i.e., $${\displaystyle (\forall {q\in Q,T\in {\mathcal {F}}})(\exists {\sigma \in \mathbf {C} })(T-\sigma )q=0}$$

Notice we did not have to directly use the machinery of matrices at all in this proof. There are other versions which do.

We can strengthen the above to the case where all the operators merely commute with their adjoint; in this case we remove the term "orthogonal" from the diagonalisation. There are weaker results for operators arising from representations due to Weyl–Peter. Let G be a fixed locally compact hausdorff group, and ${\displaystyle H=L^{2}(G)}$ (the space of square integrable measurable functions with respect to the unique-up-to-scale Haar measure on G). Consider the continuous shift action: $${\displaystyle {\begin{cases}G\times H\to H\\(gf)(x)=f(g^{-1}x)\end{cases}}}$$

Then if G were compact then there is a unique decomposition of H into a countable direct sum of finite-dimensional, irreducible, invariant subspaces (this is essentially diagonalisation of the family of operators ${\displaystyle G\subseteq U(H)}$). If G were not compact, but were abelian, then diagonalisation is not achieved, but we get a unique continuous decomposition of H into 1-dimensional invariant subspaces.

The family of Hermitian matrices is a proper subset of matrices that are unitarily diagonalizable. A matrix M is unitarily diagonalizable if and only if it is normal, i.e., M*M = MM*. Similar statements hold for compact normal operators.

Let T be compact and T*T = TT*. Apply the Cartesian decomposition to T: define $${\displaystyle R={\frac {T+T^{*}}{2}},\quad J={\frac {T-T^{*}}{2i}}.}$$

The self-adjoint compact operators R and J are called the real and imaginary parts of T, respectively. That T is compact implies that T* and, consequently, R and J are compact. Furthermore, the normality of T implies that R and J commute. Therefore they can be simultaneously diagonalized, from which follows the claim.

A hyponormal compact operator (in particular, a subnormal operator) is normal.

The spectrum of a unitary operator U lies on the unit circle in the complex plane; it could be the entire unit circle. However, if U is identity plus a compact perturbation, U has only a countable spectrum, containing 1 and possibly, a finite set or a sequence tending to 1 on the unit circle. More precisely, suppose U = I + C where C is compact. The equations UU* = U*U = I and C = U − I show that C is normal. The spectrum of C contains 0, and possibly, a finite set or a sequence tending to 0. Since U = I + C, the spectrum of U is obtained by shifting the spectrum of C by 1.

• Let H = L²([0, 1]). The multiplication operator M defined by $${\displaystyle (Mf)(x)=xf(x),\quad f\in H,\,\,x\in [0,1]}$$ is a bounded self-adjoint operator on H that has no eigenvector and hence, by the spectral theorem, cannot be compact.
• An example of a compact operator on a Hilbert space that is not self-adjoint is the Volterra operator, defined for a function ${\displaystyle f\in L^{2}([0,1])}$ and a value ${\displaystyle t\in [0,1]}$ as $${\displaystyle V(f)(t)=\int _{0}^{t}f(s)\,ds.}$$ It is the operator corresponding to the Volterra integral equations.
• Define a Hilbert-Schmidt kernel ${\displaystyle K:\Omega \times \Omega \to \mathbb {C} }$ on ${\displaystyle \Omega =[0,1]}$ and its associated Hilbert-Schmidt integral operator ${\displaystyle T_{K}:L^{2}(\Omega )\to L^{2}(\Omega )}$ as $${\displaystyle (T_{K}f)(x)=\int _{0}^{1}K(x,y)f(y)\,\mathrm {d} y.}$$ Then ${\displaystyle T_{K}}$ is a compact operator; it is a Hilbert–Schmidt operator with Hilbert-Schmidt norm ${\displaystyle \|T_{k}\|_{\mathrm {HS} }=\|K\|_{L^{2}}}$.
• ${\displaystyle T_{K}}$ is a compact self-adjoint operator if and only if ${\displaystyle K(x,y)}$ is a hermitian kernel which, according to Mercer's theorem, can be represented as $${\displaystyle K(x,y)=\sum \lambda _{n}\varphi _{n}(x){\overline {\varphi _{n}(y)}},}$$ where ${\displaystyle \{\varphi _{n}\}}$ is an orthonormal basis of eigenvectors of ${\displaystyle T_{K}}$, with eigenvalues ${\displaystyle \{\lambda _{n}\}}$ and the sum converges absolutely and uniformly on ${\displaystyle [0,1]}$.

• Calkin algebra – the quotient of the ring of bounded linear operators on the separable infinite‐dimensional Hilbert space by the ideal compact operatorsPages displaying wikidata descriptions as a fallback
• Compact operator – Type of continuous linear operator
• Decomposition of spectrum (functional analysis) – Construction in functional analysis, useful to solve differential equations − If the compactness assumption is removed, operators need not have countable spectrum in general.
• Fredholm operator – Part of Fredholm theories in integral equations
• Singular value decomposition#Bounded operators on Hilbert spaces – Matrix decomposition − The notion of singular values can be extended from matrices to compact operators.
• Spectral theory of compact operators
• Strictly singular operator

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• M. Reed and B. Simon, Methods of Modern Mathematical Physics I: Functional Analysis, Academic Press, 1972.
• Zhu, Kehe (2007), Operator Theory in Function Spaces, Mathematical surveys and monographs, vol. 138, American Mathematical Society, ISBN 978-0-8218-3965-2