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Composition operator
Composition operator
Composition operator
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In mathematics, the composition operator with symbol is a linear operator defined by the rule where denotes function composition. It is also encountered in composition of permutations in permutations groups.

The study of composition operators is covered by AMS category 47B33.

In physics

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In physics, and especially the area of dynamical systems, the composition operator is usually referred to as the Koopman operator, named after Bernard Koopman[1][2]. Its wild surge in popularity[3] is sometimes jokingly called "Koopmania"[4] - a popularity that has begun spreading in Machine Learning as well[5]. It is the left-adjoint of the transfer operator of Frobenius–Perron.

In Borel functional calculus

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Using the language of category theory, the composition operator is a pull-back on the space of measurable functions; it is adjoint to the transfer operator in the same way that the pull-back is adjoint to the push-forward; the composition operator is the inverse image functor.

Since the domain considered here is that of Borel functions, the above describes the Koopman operator as it appears in Borel functional calculus.

In holomorphic functional calculus

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The domain of a composition operator can be taken more narrowly, as some Banach space, often consisting of holomorphic functions: for example, some Hardy space or Bergman space. In this case, the composition operator lies in the realm of some functional calculus, such as the holomorphic functional calculus.

Interesting questions posed in the study of composition operators often relate to how the spectral properties of the operator depend on the function space. Other questions include whether is compact or trace-class; answers typically depend on how the function behaves on the boundary of some domain.

When the transfer operator is a left-shift operator, the Koopman operator, as its adjoint, can be taken to be the right-shift operator. An appropriate basis, explicitly manifesting the shift, can often be found in the orthogonal polynomials. When these are orthogonal on the real number line, the shift is given by the Jacobi operator.[6] When the polynomials are orthogonal on some region of the complex plane (viz, in Bergman space), the Jacobi operator is replaced by a Hessenberg operator.[7]

Applications

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In mathematics, composition operators commonly occur in the study of shift operators, for example, in the Beurling–Lax theorem and the Wold decomposition. Shift operators can be studied as one-dimensional spin lattices. Composition operators appear in the theory of Aleksandrov–Clark measures.

The eigenvalue equation of the composition operator is Schröder's equation, and the principal eigenfunction is often called Schröder's function or Koenigs function.

The composition operator has been used in data-driven techniques for dynamical systems in the context of dynamic mode decomposition algorithms, which approximate the modes and eigenvalues of the composition operator.

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Composition operator

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In functional analysis and complex analysis, a composition operator CϕC_\phi is a linear operator defined on a space of holomorphic functions HH over a domain Ω⊂C\Omega \subset \mathbb{C} by Cϕf=f∘ϕC_\phi f = f \circ \phi, where ϕ:Ω→Ω\phi: \Omega \to \Omega is a fixed holomorphic self-map of the domain. These operators arise naturally when studying how composition with a symbol ϕ\phi affects the structure and properties of function spaces, transforming abstract operator theory into concrete examples tied to the geometry and analytic behavior of ϕ\phi. Composition operators were first systematically explored in the context of Hardy spaces HpH^p (for 0<p≤∞0 < p \leq \infty) and Bergman spaces on the unit disk D={z∈C:∣z∣<1}\mathbb{D} = \{z \in \mathbb{C} : |z| < 1\}, where ϕ\phi is typically assumed to map D\mathbb{D} into itself to ensure the operator maps the space to itself. A foundational result, Littlewood's subordination principle, guarantees that if ϕ(D)⊂D\phi(\mathbb{D}) \subset \mathbb{D}, then CϕC_\phi is bounded on HpH^p for all p>0p > 0, as the composition preserves the boundedness and integrability properties of the functions in these spaces. This principle, established in 1925, underpins much of the theory and highlights the operators' role in subordination chains of analytic functions. Key aspects of composition operators include their boundedness, compactness, and spectral properties, which depend intricately on the dynamics of ϕ\phi, such as fixed points and boundary behavior. For instance, a necessary condition for CϕC_\phi to be compact on H2H^2 is that ϕ\phi has no finite angular derivative at any point on the unit circle, providing a link between the operator's essential spectrum and the symbol's extension to the boundary. These operators have applications in ergodic theory and model spaces, but their primary significance lies in illuminating broader questions in operator algebras and function theory.

Definition and Properties

Definition

In mathematics, particularly in the field of operator theory, a composition operator is a linear transformation induced by function composition on a space of functions. Given a domain DD in the complex plane and a fixed symbol function ϕ:D→D\phi: D \to D, the composition operator CϕC_\phi acts on a space of functions F(D)F(D) by Cϕf=f∘ϕC_\phi f = f \circ \phi for each f∈F(D)f \in F(D). This construction defines CϕC_\phi as an operator on the vector space F(D)F(D), where the symbol ϕ\phi remains fixed while varying over the family of functions in F(D)F(D). The operator CϕC_\phi is linear, satisfying Cϕ(αf+βg)=α(Cϕf)+β(Cϕg)C_\phi(\alpha f + \beta g) = \alpha (C_\phi f) + \beta (C_\phi g) for scalars α,β\alpha, \beta and functions f,g∈F(D)f, g \in F(D), since composition distributes over scalar multiplication and addition in the function space. Unlike general function composition, which pairs two varying functions, the composition operator emphasizes the perspective of ϕ\phi as a fixed inducing map that generates a well-defined action on the entire space F(D)F(D). The study of composition operators falls under the American Mathematical Society (AMS) subject classification 47B33. These operators originated in mid-20th-century investigations of analytic function spaces, with the systematic study initiated by R. P. Nordgren in 1968.

Basic Properties

The composition operator CϕC_\phi induced by a holomorphic self-map ϕ\phi of a domain Ω\Omega on a space of analytic functions on Ω\Omega is injective if and only if ϕ\phi is surjective onto Ω\Omega. This condition ensures that distinct functions in the space are mapped to distinct compositions, as non-surjectivity would allow kernels to form from functions vanishing on the image of ϕ\phi. In more general settings, such as Orlicz spaces over measure spaces, the same injectivity criterion holds when ϕ\phi (or the inducing transformation TT) is surjective, preventing the operator from collapsing distinct elements. In Hilbert spaces of functions, such as L2(μ)L^2(\mu) over a measure space, the adjoint Cϕ∗C_\phi^* of the composition operator CϕC_\phi corresponds to the Perron-Frobenius operator (also known as the transfer operator) associated with ϕ\phi. This adjoint propagates densities forward under the dynamics induced by ϕ\phi, contrasting with the backward action of CϕC_\phi on observables, and plays a key role in ergodic theory and spectral analysis of dynamical systems. The explicit form of Cϕ∗C_\phi^* often involves integration against the Jacobian or Radon-Nikodym derivative of ϕ\phi with respect to the measure μ\mu, ensuring duality in the inner product structure. Composition operators form a representation of the semigroup of holomorphic self-maps under function composition, satisfying Cψ∘Cϕ=Cϕ∘ψC_\psi \circ C_\phi = C_{\phi \circ \psi} for compatible symbols ϕ\phi and ψ\psi. This algebraic property highlights their role in embedding transformation semigroups into the operator algebra, facilitating the study of iterates and powers of CϕC_\phi via iterates of ϕ\phi. The relation underscores the non-commutativity typical of such operators unless ϕ\phi and ψ\psi commute pointwise. For norm estimates, in spaces equipped with the supremum norm, such as the space of continuous functions C(Ω‾)C(\overline{\Omega}) where ϕ(Ω)⊂Ω\phi(\Omega) \subset \Omega, the operator satisfies ∥Cϕf∥∞≤∥f∥∞\|C_\phi f\|_\infty \leq \|f\|_\infty for all ff, implying ∥Cϕ∥≤1\|C_\phi\| \leq 1. In analytic function spaces like the Hardy space H∞(Ω)H^\infty(\Omega), the same bound holds under the assumption that ϕ\phi maps Ω\Omega into itself, with equality often achieved. In cases where ϕ\phi is differentiable, refined estimates may incorporate the supremum of ∣ϕ′(z)∣|\phi'(z)| over Ω\Omega, providing tighter bounds in weighted or Bergman-type norms, though the universal upper bound of 1 prevails in unweighted sup-norm settings. Trivial cases illustrate these properties simply: if ϕ\phi is the identity map on Ω\Omega, then CϕC_\phi coincides with the identity operator on the function space. Conversely, if ϕ\phi maps Ω\Omega entirely outside Ω\Omega (violating the domain inclusion), CϕC_\phi reduces to the zero operator, as compositions are undefined or vanish on the relevant domain. These extremes bound the spectrum and norm behaviors observed in non-trivial symbols.

Composition Operators in Function Spaces

In Holomorphic Function Spaces

Composition operators play a significant role in the study of holomorphic function spaces, particularly the Hardy spaces Hp(D)H^p(\mathbb{D}) and Hp(H)H^p(\mathbb{H}) on the unit disk D\mathbb{D} or upper half-plane H\mathbb{H}, and the Bergman spaces Ap(D)A^p(\mathbb{D}) on the disk, where p≥1p \geq 1. These spaces consist of holomorphic functions satisfying integrability conditions with respect to specific measures, such as the normalized Lebesgue area measure for Bergman spaces or boundary Poisson integrals for Hardy spaces. The operator Cϕf=f∘ϕC_\phi f = f \circ \phi, induced by a holomorphic self-map ϕ\phi of the domain, preserves holomorphy and maps these spaces into themselves under suitable conditions on ϕ\phi. Boundedness of CϕC_\phi on the Hardy space H2(D)H^2(\mathbb{D}) holds if and only if ϕ\phi is analytic in D\mathbb{D} with ∣ϕ(z)∣≤1|\phi(z)| \leq 1 for ∣z∣<1|z| < 1, a consequence of the Littlewood subordination principle, which ensures that f∘ϕf \circ \phi inherits the square-integrability of ff via probabilistic subordination of measures. Similar criteria apply to H2(H)H^2(\mathbb{H}), where ϕ\phi must map H\mathbb{H} into itself, and boundedness is equivalent to ϕ\phi having a finite angular derivative at infinity. On Bergman spaces A2(D)A^2(\mathbb{D}), boundedness requires ϕ(D)⊂D\phi(\mathbb{D}) \subset \mathbb{D} and the pullback measure ∣ϕ∗∣dA|\phi^*| dA to be a Carleson measure, controlling the growth of ϕ\phi near the boundary. These conditions highlight the analytic constraints distinguishing holomorphic settings from more general function spaces. Compactness of CϕC_\phi on H2(D)H^2(\mathbb{D}) or A2(D)A^2(\mathbb{D}) requires stricter boundary behavior: ϕ(z)\phi(z) must tend to the boundary ∂D\partial \mathbb{D} as ∣z∣→1−|z| \to 1^-, except possibly at points where the angular derivative ϕ′(ζ)\phi'(\zeta) exists and is infinite for ζ∈∂D\zeta \in \partial \mathbb{D}. This angular derivative condition ensures that CϕC_\phi approximates finite-rank operators, as sequences of normalized reproducing kernels at points approaching the boundary map to functions vanishing uniformly. These geometric criteria, involving nontangential limits and derivative bounds, underscore the role of radial or angular approach in compactness. The essential spectrum of CϕC_\phi on H2(D)H^2(\mathbb{D}) is determined by the essential range of the boundary function ϕ∣∂D\phi|_{\partial \mathbb{D}}, specifically the closure of {ϕ(eiθ):θ∈[0,2π)}\{\phi(e^{i\theta}) : \theta \in [0, 2\pi)\} union {0}\{0\} when ϕ\phi is nonconstant, modulated by Carleson measure conditions on the pullback of the boundary measure. This reflects how the operator's Fredholm properties depend on the distribution of ϕ\phi's values near the distinguished boundary, with holes in the range corresponding to points of local injectivity or measure-zero sets. For Bergman spaces, analogous results hold, where the essential spectrum incorporates the area measure's interaction with ϕ\phi's boundary image. An illustrative example is the identity operator on H2(D)H^2(\mathbb{D}), obtained with the symbol ϕ(z)=z\phi(z) = z, which is an isometry with spectrum {1}\{1\}. More generally, composition operators on Hardy spaces can be unitarily equivalent to weighted shifts on ℓ2\ell^2, where the weights derive from the moduli of Herglotz integral representations or iterates of ϕ\phi, capturing multiplication by boundary functions in the corona. Trace-class membership of CϕC_\phi on H2(D)H^2(\mathbb{D}) or A2(D)A^2(\mathbb{D}) is characterized by integrability conditions on the Nevanlinna counting function Nϕ(w)=∑ϕ(ak)=w(1−∣ak∣2)N_\phi(w) = \sum_{\phi(a_k)=w} (1 - |a_k|^2), which counts preimages of w \in \mathbb{D}&#36; weighted by hyperbolic distances. Specifically, C_\phibelongstothetraceclassifbelongs to the trace class if\int_{\mathbb{D}} N_\phi(w) (1 - |w|^2)^{-1} dA(w) < \infty,ensuringthesingularvaluesdecaysufficientlyfastviakernelestimates.ThiscriterionextendsSchatten, ensuring the singular values decay sufficiently fast via kernel estimates. This criterion extends Schatten p−classmembershipfor-class membership for p > 1,linkingoperatoridealstoglobalanalyticpropertiesof, linking operator ideals to global analytic properties of \phi$.

In Measurable Function Spaces

In measurable function spaces, the composition operator induced by a measurable transformation ϕ:X→X\phi: X \to X on a σ\sigma-finite measure space (X,B,μ)(X, \mathcal{B}, \mu) is defined as Cϕf=f∘ϕC_\phi f = f \circ \phi for f∈Lp(μ)f \in L^p(\mu), where 1≤p≤∞1 \leq p \leq \infty, provided ϕ\phi is nonsingular, meaning μ∘ϕ−1≪μ\mu \circ \phi^{-1} \ll \mu. For p=∞p = \infty, the operator maps L∞(μ)L^\infty(\mu) to itself under nonsingularity alone. For 1≤p<∞1 \leq p < \infty, it maps Lp(μ)L^p(\mu) to itself if the Radon-Nikodym derivative w=d(μ∘ϕ−1)dμ∈L∞(μ)w = \frac{d(\mu \circ \phi^{-1})}{d\mu} \in L^\infty(\mu), preserving the equivalence classes of measurable functions, and arises naturally in the study of transformations that alter the argument of integrable functions while respecting the measure structure. For boundedness, CϕC_\phi is bounded on Lp(μ)L^p(\mu) for 1≤p<∞1 \leq p < \infty if ϕ\phi is nonsingular and the Radon-Nikodym derivative w=d(μ∘ϕ−1)dμw = \frac{d(\mu \circ \phi^{-1})}{d\mu} belongs to L∞(μ)L^\infty(\mu), in which case ∥Cϕ∥=∥w∥∞1/p\|C_\phi\| = \|w\|_\infty^{1/p}. For p=∞p = \infty, CϕC_\phi is bounded with norm 1 under nonsingularity alone. If ϕ\phi is measure-preserving, then w≡1w \equiv 1 almost everywhere, so CϕC_\phi is an isometry on Lp(μ)L^p(\mu) for all 1≤p≤∞1 \leq p \leq \infty, satisfying ∥Cϕf∥p=∥f∥p\|C_\phi f\|_p = \|f\|_p for every f∈Lp(μ)f \in L^p(\mu). A concrete example occurs on Lp([0,1])L^p([0,1]) with Lebesgue measure, where the doubling map ϕ(x)=2xmod  1\phi(x) = 2x \mod 1 is a nonsingular (in fact, measure-preserving) transformation that induces a bounded composition operator CϕC_\phi, closely tied to ergodic theory through its mixing properties and role in analyzing invariant measures. Regarding compactness, the weak compactness of a sequence of composition operators {Cϕn}\{C_{\phi_n}\} on Lp(μ)L^p(\mu) (for 1<p<∞1 < p < \infty) is characterized by the uniform integrability of the families {∣f∘ϕn∣}\{|f \circ \phi_n|\} for all ff in the unit ball of Lp(μ)L^p(\mu), ensuring relative weak compactness in the operator topology via connections to mean ergodic projections and orbit behavior. In probability spaces, where μ(X)=1\mu(X) = 1, these operators link directly to induced maps on observables, as CϕC_\phi describes how expectations of measurable functions (observables) transform under ϕ\phi, facilitating the study of dynamical systems through spectral and ergodic decompositions.

Role in Functional Calculus

Holomorphic Functional Calculus

The holomorphic functional calculus provides a framework for defining f(T) for a normal operator T on a Hilbert space and a function f holomorphic on an open set containing the spectrum σ(T). This is achieved via the Riesz-Dunford integral formula: f(T)=12πi∫Γf(λ)(λI−T)−1 dλ,f(T) = \frac{1}{2\pi i} \int_\Gamma f(\lambda) (\lambda I - T)^{-1} \, d\lambda, where Γ is a positively oriented contour enclosing σ(T) in its interior. This construction ensures that the map f ↦ f(T) is an algebra homomorphism from the space of such holomorphic functions to the bounded operators, preserving addition, scalar multiplication, and composition of functions. Composition operators CϕC_\phi, defined by (Cϕg)(z)=g(ϕ(z))(C_\phi g)(z) = g(\phi(z)) for an analytic self-map ϕ\phi of the domain, play a role in this framework by facilitating the composition of symbols in the functional calculus. For a normal multiplication operator T=MψT = M_\psi on a space of holomorphic functions, where ψ\psi is a suitable multiplier, the functional calculus yields Mf∘ψ=f(Mψ)M_{f \circ \psi} = f(M_\psi). The intertwining relation CϕMψ=Mψ∘ϕCϕC_\phi M_\psi = M_{\psi \circ \phi} C_\phi holds for bounded composition operators on holomorphic function spaces, allowing the transfer of spectral properties across the calculus. The spectral mapping theorem underpins these interactions: for compatible holomorphic f and ϕ\phi, σ(f(ϕ(T)))=f(ϕ(σ(T)))\sigma(f(\phi(T))) = f(\phi(\sigma(T))). This equality follows from the homomorphism property of the calculus and the fact that σ(ϕ(T))=ϕ(σ(T))\sigma(\phi(T)) = \phi(\sigma(T)) when T is normal and ϕ\phi is holomorphic on a suitable domain. In this context, the composition operator CϕC_\phi effectively induces the symbol shift ϕ\phi in the spectral picture. A prominent example arises with shift operators on Hardy spaces H2(D)H^2(\mathbb{D}), where the forward shift T=MzT = M_z (multiplication by z) admits a functional calculus via H∞H^\infty functions, with f(T)=Mff(T) = M_f for f∈H∞f \in H^\infty. Composition operators CϕC_\phi on H2H^2 relate to this through their action on invariant subspaces, intertwining multiplications and preserving the structure of the calculus. Specifically, the Beurling-Lax-Halmos theorem characterizes the wandering subspaces for completely nonunitary contractions like powers of the shift as model spaces Kθ=H2⊖θH2K_\theta = H^2 \ominus \theta H^2, where θ\theta is an inner function; this enables the representation of composition-induced shifts as Toeplitz operators with analytic symbols, linking CϕC_\phi to the spectral decomposition in the functional calculus. Recent extensions of this framework to several complex variables employ hyperholomorphic or slice-regular functional calculi, allowing composition operators on polydiscs or balls to interact with multi-operator tuples via multivariable Riesz-Dunford integrals, though full spectral mapping properties remain under active investigation.

Borel Functional Calculus

In the Borel functional calculus, for a self-adjoint operator TT on a Hilbert space, the application of a Borel measurable function f:R→Cf: \mathbb{R} \to \mathbb{C} to TT is defined via the spectral theorem as f(T)=∫Rf(λ) dE(λ)f(T) = \int_{\mathbb{R}} f(\lambda) \, dE(\lambda), where EE is the unique spectral measure associated to TT satisfying T=∫Rλ dE(λ)T = \int_{\mathbb{R}} \lambda \, dE(\lambda). This construction extends the continuous functional calculus to all bounded Borel functions while preserving the *-homomorphism property and weak continuity. Composition operators play a role in this framework as pull-backs on the space of Borel functions. Specifically, for a Borel measurable map ϕ:R→R\phi: \mathbb{R} \to \mathbb{R}, the composition operator CϕC_\phi acts on functions by Cϕg=g∘ϕC_\phi g = g \circ \phi, and in the Koopman setting for measure-preserving transformations, where CϕC_\phi is unitary, the conjugation satisfies Cϕf(T)Cϕ−1=f(ϕ(T))C_\phi f(T) C_\phi^{-1} = f(\phi(T)), with ϕ(T)\phi(T) defined via the Borel calculus assuming ϕ\phi is real-valued to preserve self-adjointness. This pull-back operation is adjoint to the push-forward, or transfer operator, which maps measures forward under ϕ\phi and preserves the spectral structure. The inverse image under CϕC_\phi defines a functor on Borel functions that maintains measurability, as the preimage of Borel sets under ϕ\phi remains Borel measurable. In ergodic systems, the composition operator aligns with the Koopman action, where for a measure-preserving transformation ϕ\phi, the Koopman operator satisfies Cϕf=f∘ϕC_\phi f = f \circ \phi, embedding the dynamics into the unitary representation on L2L^2. This connection highlights how composition operators facilitate the transfer of spectral measures in measurable resolutions of self-adjoint operators. A key feature of the Borel functional calculus is the uniqueness of its extension from continuous functions to all Borel functions, ensured by the density of continuous functions in the μ\mu-topology and the continuity of the map from Borel functions to bounded operators in the weak operator topology. This uniqueness theorem underpins the consistency of composition-induced operations in the calculus.

Applications

In Dynamical Systems and Physics

In dynamical systems, the composition operator manifests as the Koopman operator, which provides a linear embedding of nonlinear dynamics by acting on observables. For a continuous-time flow ϕt:X→X\phi_t: X \to X generated by a vector field on a phase space XX, the Koopman operator KtK_t is defined as (Ktf)(x)=f(ϕt(x))(K_t f)(x) = f(\phi_t(x)) for observable functions ff on XX, typically in a suitable function space such as L2(X,μ)L^2(X, \mu) with respect to an invariant measure μ\mu. This construction linearizes the nonlinear evolution of states by instead evolving observables linearly, enabling spectral analysis and global approximations of complex behaviors. The Koopman operator was introduced by Bernard O. Koopman in 1931 specifically for Hamiltonian systems, where it transforms the nonlinear equations of motion into a linear unitary operator on a Hilbert space of observables, preserving energy and aiding in the resolution of ergodic questions. In physics, it has found applications in modeling the time evolution of observables, particularly in quantum mechanics, where its unitary action mirrors the Heisenberg picture of operator evolution under Hamiltonian flows. A surge in interest, dubbed "Koopmania," has driven its use in fluid dynamics to decompose turbulent flows into coherent structures via spectral methods like dynamic mode decomposition. The Koopman operator is the left adjoint (or dual) to the Perron-Frobenius transfer operator, which advances probability densities under the same dynamics; this duality links observable evolution to measure transport, with shared spectra that underpin ergodic theory. For example, in classical mechanics with measure-preserving transformations, the Koopman operator is unitary on L2(X,μ)L^2(X, \mu), ensuring the preservation of inner products and enabling decompositions into eigenmodes that quantify stability and mixing. Applications to quantum chaos have grown in the 2020s, with efforts exploring Koopman embeddings and dual-Koopman circuits for spectral properties and many-body systems.

In Operator Theory

In operator theory, the Wold decomposition theorem provides a fundamental structure for isometric weighted composition operators Wψ,ϕW_{\psi, \phi} on the Hardy space H2H^2 of the unit disk. When Wψ,ϕW_{\psi, \phi} is an isometry and the symbol ϕ\phi fixes a point pp in the disk with ∣ψ(p)∣=1|\psi(p)|=1, the space decomposes as H2=\Span{Kp}⊕αpH2H^2 = \Span\{K_p\} \oplus \alpha_p H^2, where KpK_p is the reproducing kernel at pp and αp\alpha_p is the Blaschke factor corresponding to pp. This splitting separates the unitary component on the finite-dimensional invariant subspace \Span{Kp}\Span\{K_p\} from the pure isometry on the infinite-dimensional backward shift part αpH2\alpha_p H^2. In cases where ϕ\phi is a disk automorphism and inner, the operator aligns with a unitary conjugation, yielding a forward shift structure if ϕ\phi is elliptic or the identity (nonconstant). The Beurling-Lax theorem, originally characterizing shift-invariant subspaces of H2H^2 as θH2\theta H^2 for inner functions θ\theta, extends to analyze invariant subspaces under composition operators. A Beurling-type subspace θH2\theta H^2 is invariant under CϕC_\phi if and only if θ∘ϕ/θ\theta \circ \phi / \theta belongs to the Schur class of bounded analytic functions on the disk. This condition links the lattice of invariant subspaces to factorization properties via the Riesz theorem, with cyclic vectors for CϕC_\phi corresponding to outer-like functions that generate dense orbits, mirroring the role of outer functions as cyclic vectors for the shift operator. Such relations illuminate the structure of minimal invariant subspaces and the cyclic behavior of CϕC_\phi. For isometric CϕC_\phi on H2H^2, Aleksandrov-Clark measures offer a spectral representation through the Herglotz theorem. These measures, indexed by α∈R\alpha \in \mathbb{R}, arise from harmonic functions vα(z)=1π(1+α2)ℜ(i1+αϕ(z)ϕ(z)−α)v_\alpha(z) = \frac{1}{\pi(1+\alpha^2)} \Re \left( i \frac{1 + \alpha \phi(z)}{\phi(z) - \alpha} \right) for symbols ϕ:C+→C+\phi: \mathbb{C}^+ \to \mathbb{C}^+, admitting the integral form vα(x+iy)=cαy+∫1πy(x−t)2+y2dμα(t)v_\alpha(x+iy) = c_\alpha y + \int \frac{1}{\pi} \frac{y}{(x-t)^2 + y^2} d\mu_\alpha(t) over the real line, where μα\mu_\alpha are the Clark measures. This framework enables the Herglotz representation of the Poisson integral, facilitating the study of the operator's unitary extensions and boundary behavior. Composition operators also feature in lattice models for quantum spin chains, where shift compositions model the dynamics of one-dimensional spin lattices, generalizing the unilateral shift to capture interactions in infinite-degree-of-freedom systems. Schröder's equation, the eigenvalue problem λψ=ψ∘ϕ\lambda \psi = \psi \circ \phi for CϕC_\phi, yields eigenvalues as powers of the multiplier ϕ′(a)\phi'(a) at a fixed point aa with 0<∣ϕ′(a)∣<10 < |\phi'(a)| < 1, each of multiplicity one. The principal eigenfunction, the Koenigs function σ(z)=lim⁡n→∞ϕn(z)/[ϕ′(a)]n\sigma(z) = \lim_{n \to \infty} \phi^n(z) / [\phi'(a)]^n, conjugates ϕ\phi to multiplication by ϕ′(a)\phi'(a) on σ(D)\sigma(\mathbb{D}), providing univalent solutions when ϕ\phi is univalent. Extensions of these results to multivariable settings, such as the polydisk or ball, remain incomplete, with notable gaps in general decompositions and measure theory post-2015. While boundedness and compactness of multivariable composition operators have been characterized in specific weighted spaces, full analogs of the Wold and Aleksandrov-Clark frameworks for joint symbols lack comprehensive development, limiting applications to non-separable Hilbert spaces.

In Machine Learning and Data Analysis

In machine learning and data analysis, composition operators, particularly the Koopman operator, enable the linearization of nonlinear dynamical systems through data-driven approximations, facilitating tasks such as system identification and forecasting from observational snapshots. The Koopman operator, defined as a composition operator Kf=f∘ϕ\mathcal{K} f = f \circ \phi where ϕ\phi is the dynamical flow and ff is an observable function, transforms nonlinear evolution into linear dynamics in a lifted function space, allowing standard linear techniques to be applied to complex data. This approach has gained prominence in handling high-dimensional time-series data, where direct nonlinear modeling is computationally prohibitive. Dynamic mode decomposition (DMD) serves as a foundational data-driven method to approximate the Koopman operator from sequential data snapshots, extracting spatial-temporal modes that capture system evolution without requiring an explicit model of the underlying dynamics. Introduced for fluid dynamics analysis, DMD constructs a finite-dimensional matrix approximation of the Koopman operator by performing an eigendecomposition on the data matrix formed from time-shifted snapshots, yielding modes, eigenvalues, and eigenvectors that predict future states. For instance, in system identification, DMD processes measurement data to reveal dominant coherent structures, achieving accurate short-term predictions with reduced computational cost compared to full nonlinear simulations. Extended dynamic mode decomposition (EDMD) enhances this by incorporating a user-defined dictionary of nonlinear basis functions, such as polynomials or radial basis functions, to better approximate the infinite-dimensional Koopman operator in a finite basis, improving accuracy for strongly nonlinear systems. EDMD computes the Koopman matrix via least-squares projection onto the dictionary, enabling dimensionality reduction while preserving key dynamical invariants. In time-series analysis, Koopman-based methods like those in the "Koopmania" framework provide dimensionality reduction by embedding high-dimensional data into a lower-dimensional linear subspace via learned eigenfunctions, outperforming traditional techniques such as PCA for capturing temporal correlations in multivariate sequences. This is particularly useful for forecasting in domains like finance or climate modeling, where EDMD variants reduce the state space while maintaining predictive fidelity, as demonstrated by spectral decompositions that forecast ergodic dynamical systems on benchmark chaotic datasets. Recent advancements integrate neural networks to learn the embedding and Koopman operator end-to-end; for example, neural Koopman operators parameterize the dictionary and linear dynamics using deep architectures, enabling scalable control and prediction in robotic systems. These post-2020 developments, including Hamiltonian neural Koopman operators (as of 2024), incorporate physical constraints like energy conservation to enhance generalization in AI-physics integration tasks. An illustrative application appears in signal processing, where composition operators facilitate nonlinear filtering by composing observable functions with system dynamics to denoise or reconstruct signals, as in implicit algorithms that solve filtering problems through iterative operator approximations, reducing mean-squared error in noisy environments by leveraging the linearity in the observable space. Computationally, matrix approximations of the composition operator CϕC_\phi are obtained by projecting onto finite dictionaries, forming a matrix K≈G†AK \approx G^\dagger A, where GG and AA are Gramian-like matrices from dictionary evaluations on data pairs, allowing efficient eigenvalue computations for modal analysis with dictionary sizes as low as 100 functions for high-fidelity approximations.

References

  1. https://www.math.purdue.edu/~cowen/BridgRes1202.pdf
  2. https://www.sciencedirect.com/topics/mathematics/composition-operator
  3. https://cab.unime.it/journals/index.php/congress/article/download/66/66
  4. https://users.math.msu.edu/users/shapiro/Pubvit/Downloads/Smirnov/Smirnov.pdf
  5. https://www.sciencedirect.com/science/article/pii/S0304020801800426
  6. https://www.cambridge.org/core/journals/canadian-journal-of-mathematics/article/composition-operators/7A8444238EA2A78F670EB83B800B5EBE
  7. https://zbmath.org/static/msc2020.pdf
  8. https://link.springer.com/article/10.1007/s13163-016-0214-1
  9. https://www.cambridge.org/core/journals/canadian-journal-of-mathematics/article/angular-derivatives-and-compact-composition-operators-on-the-hardy-and-bergman-spaces/7CE085B26553255EA0F742BB279AE4E3
  10. https://arxiv.org/abs/1307.2652
  11. https://www.sciencedirect.com/science/article/pii/S0022123615004577
  12. https://doi.org/10.7153/oam-2023-17-13
  13. https://www.wiley.com/en-us/Real+Analysis%3A+Modern+Techniques+and+Their+Applications%2C+2nd+Edition-p-9780471317162
  14. https://www.math.uni-kiel.de/isem21/en/course/phase1/isem21-2017-11-29-from-chapter01-to-chapter06
  15. https://link.springer.com/article/10.1007/s00006-021-01148-1
  16. https://www.mathematik.tu-darmstadt.de/media/analysis/lehrmaterial_anapde/hallerd/ISem21complete.pdf
  17. https://arxiv.org/abs/2102.12086
  18. https://iopscience.iop.org/article/10.1088/1751-8121/ac7d22
  19. https://arxiv.org/abs/2307.04950
  20. https://nyjm.albany.edu/j/2014/20-35p.pdf
  21. https://arxiv.org/pdf/2004.00264
  22. https://arxiv.org/pdf/0810.2184
  23. https://users.math.msu.edu/users/shapiro/Pubvit/Downloads/RieszExpo/RieszExpo.pdf
  24. https://www.aimspress.com/article/doi/10.3934/math.20231400
  25. https://cwrowley.princeton.edu/papers/Tu-DMD.pdf
  26. http://helper.ipam.ucla.edu/publications/mtws4/mtws4_12328.pdf
  27. https://mgroup.me.ucsb.edu/sites/default/files/publications/keedmd.pdf
  28. https://epubs.siam.org/doi/10.1137/17M115414X
  29. https://jmlr.csail.mit.edu/papers/volume22/20-406/20-406.pdf
  30. https://www.sciencedirect.com/science/article/pii/S1063520317300982
  31. https://ieeexplore.ieee.org/document/9790335
  32. https://link.aps.org/doi/10.1103/PhysRevResearch.6.L012031
  33. http://webhome.auburn.edu/~yzc0009/Implicit_Filtering.pdf
  34. https://mediatum.ub.tum.de/doc/1726942/fb5j2cj0lrkubpunfh4tiw3mr.pdf
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