In mathematics, an eigenfunction of a linear operator D defined on some function space is any non-zero function <math>f</math> in that space that, when acted upon by D, is only multiplied by some scaling factor called an eigenvalue. As an equation, this condition can be written as <math display="block">Df = \lambda f</math> for some scalar eigenvalue <math>\lambda.</math>Template:SfnTemplate:SfnTemplate:Sfn The solutions to this equation may also be subject to boundary conditions that limit the allowable eigenvalues and eigenfunctions.
An eigenfunction is a type of eigenvector.
EigenfunctionsEdit
In general, an eigenvector of a linear operator D defined on some vector space is a nonzero vector in the domain of D that, when D acts upon it, is simply scaled by some scalar value called an eigenvalue. In the special case where D is defined on a function space, the eigenvectors are referred to as eigenfunctions. That is, a function f is an eigenfunction of D if it satisfies the equation Template:NumBlk where λ is a scalar.Template:SfnTemplate:SfnTemplate:Sfn The solutions to Equation Template:EqNote may also be subject to boundary conditions. Because of the boundary conditions, the possible values of λ are generally limited, for example to a discrete set λ1, λ2, … or to a continuous set over some range. The set of all possible eigenvalues of D is sometimes called its spectrum, which may be discrete, continuous, or a combination of both.Template:Sfn
Each value of λ corresponds to one or more eigenfunctions. If multiple linearly independent eigenfunctions have the same eigenvalue, the eigenvalue is said to be degenerate and the maximum number of linearly independent eigenfunctions associated with the same eigenvalue is the eigenvalue's degree of degeneracy or geometric multiplicity.Template:SfnTemplate:Sfn
Derivative exampleEdit
A widely used class of linear operators acting on infinite dimensional spaces are differential operators on the space C∞ of infinitely differentiable real or complex functions of a real or complex argument t. For example, consider the derivative operator <math display="inline" alt="d over dt">\frac{d}{dt}</math> with eigenvalue equation <math display="block" alt="the derivative of f of t equals lambda times f of t"> \frac{d}{dt}f(t) = \lambda f(t).</math>
This differential equation can be solved by multiplying both sides by <math display="inline" alt="dt over f of t">\frac{dt}{f(t)}</math> and integrating. Its solution, the exponential function <math display="block" alt="f of t equals f nought times e raised to lambda t"> f(t)=f_0 e^{\lambda t},</math> is the eigenfunction of the derivative operator, where f0 is a parameter that depends on the boundary conditions. Note that in this case the eigenfunction is itself a function of its associated eigenvalue λ, which can take any real or complex value. In particular, note that for λ = 0 the eigenfunction f(t) is a constant.
Suppose in the example that f(t) is subject to the boundary conditions f(0) = 1 and <math display="inline" alt="df over dt at t equals 0 is 2">\left.\frac{df}{dt}\right|_{t=0} = 2</math>. We then find that <math display="block" alt="f of t equals e raised to 2t"> f(t)=e^{2t},</math> where λ = 2 is the only eigenvalue of the differential equation that also satisfies the boundary condition.
Link to eigenvalues and eigenvectors of matricesEdit
Eigenfunctions can be expressed as column vectors and linear operators can be expressed as matrices, although they may have infinite dimensions. As a result, many of the concepts related to eigenvectors of matrices carry over to the study of eigenfunctions.
Define the inner product in the function space on which D is defined as <math display="block"> \langle f,g \rangle = \int_{\Omega} \ f^*(t)g(t) dt,</math> integrated over some range of interest for t called Ω. The * denotes the complex conjugate.
Suppose the function space has an orthonormal basis given by the set of functions {u1(t), u2(t), …, un(t)}, where n may be infinite. For the orthonormal basis, <math display="block"> \langle u_i,u_j \rangle = \int_{\Omega} \ u_i^*(t)u_j(t) dt = \delta_{ij} = \begin{cases}
1 & i=j \\ 0 & i \ne j
\end{cases},</math> where δij is the Kronecker delta and can be thought of as the elements of the identity matrix.
Functions can be written as a linear combination of the basis functions, <math display="block">f(t) = \sum_{j=1}^n b_j u_j(t),</math> for example through a Fourier expansion of f(t). The coefficients bj can be stacked into an n by 1 column vector Template:Nowrap. In some special cases, such as the coefficients of the Fourier series of a sinusoidal function, this column vector has finite dimension.
Additionally, define a matrix representation of the linear operator D with elements <math display="block"> A_{ij} = \langle u_i,Du_j \rangle = \int_{\Omega}\ u^*_i(t)Du_j(t) dt.</math>
We can write the function Df(t) either as a linear combination of the basis functions or as D acting upon the expansion of f(t), <math display="block">Df(t) = \sum_{j=1}^n c_j u_j(t) = \sum_{j=1}^n b_j Du_j(t).</math>
Taking the inner product of each side of this equation with an arbitrary basis function ui(t), <math display="block">\begin{align}
\sum_{j=1}^n c_j \int_{\Omega} \ u_i^*(t)u_j(t) dt &= \sum_{j=1}^n b_j \int_{\Omega} \ u_i^*(t)Du_j(t) dt, \\
c_i &= \sum_{j=1}^n b_j A_{ij}.
\end{align}</math>
This is the matrix multiplication Ab = c written in summation notation and is a matrix equivalent of the operator D acting upon the function f(t) expressed in the orthonormal basis. If f(t) is an eigenfunction of D with eigenvalue λ, then Ab = λb.
Eigenvalues and eigenfunctions of Hermitian operatorsEdit
Many of the operators encountered in physics are Hermitian. Suppose the linear operator D acts on a function space that is a Hilbert space with an orthonormal basis given by the set of functions {u1(t), u2(t), …, un(t)}, where n may be infinite. In this basis, the operator D has a matrix representation A with elements <math display="block"> A_{ij} = \langle u_i,Du_j \rangle = \int_{\Omega} dt\ u^*_i(t)Du_j(t).</math> integrated over some range of interest for t denoted Ω.
By analogy with Hermitian matrices, D is a Hermitian operator if Aij = Aji*, or:Template:Sfn <math display="block">\begin{align} \langle u_i,Du_j \rangle &= \langle Du_i,u_j \rangle, \\[-1pt] \int_{\Omega} dt\ u^*_i(t)Du_j(t) &= \int_{\Omega} dt\ u_j(t)[Du_i(t)]^*. \end{align}</math>
Consider the Hermitian operator D with eigenvalues λ1, λ2, … and corresponding eigenfunctions f1(t), f2(t), …. This Hermitian operator has the following properties:
- Its eigenvalues are real, λi = λi*Template:SfnTemplate:Sfn
- Its eigenfunctions obey an orthogonality condition, <math alt="inner product of f sub i and f sub j equals 0">\langle f_i,f_j \rangle = 0 </math> if i ≠ jTemplate:SfnTemplate:SfnTemplate:Sfn
The second condition always holds for λi ≠ λj. For degenerate eigenfunctions with the same eigenvalue λi, orthogonal eigenfunctions can always be chosen that span the eigenspace associated with λi, for example by using the Gram-Schmidt process.Template:Sfn Depending on whether the spectrum is discrete or continuous, the eigenfunctions can be normalized by setting the inner product of the eigenfunctions equal to either a Kronecker delta or a Dirac delta function, respectively.Template:SfnTemplate:Sfn
For many Hermitian operators, notably Sturm–Liouville operators, a third property is
- Its eigenfunctions form a basis of the function space on which the operator is definedTemplate:Sfn
As a consequence, in many important cases, the eigenfunctions of the Hermitian operator form an orthonormal basis. In these cases, an arbitrary function can be expressed as a linear combination of the eigenfunctions of the Hermitian operator.
ApplicationsEdit
Vibrating stringsEdit
Let Template:Math denote the transverse displacement of a stressed elastic chord, such as the vibrating strings of a string instrument, as a function of the position Template:Mvar along the string and of time Template:Mvar. Applying the laws of mechanics to infinitesimal portions of the string, the function Template:Mvar satisfies the partial differential equation <math display="block" alt="the second partial derivative of h with respect to t equals c squared times the second partial derivative of h with respect to x">\frac{\partial^2 h}{\partial t^2} = c^2\frac{\partial^2 h}{\partial x^2},</math> which is called the (one-dimensional) wave equation. Here Template:Mvar is a constant speed that depends on the tension and mass of the string.
This problem is amenable to the method of separation of variables. If we assume that Template:Math can be written as the product of the form Template:Math, we can form a pair of ordinary differential equations: <math display="block" alt="d square big X over d x squared equals negative of omega over c quantity squared times big X, and d squared big T over d t squared equals negative omega squared times T">\frac{d^2}{dx^2}X=-\frac{\omega^2}{c^2}X, \qquad \frac{d^2}{dt^2}T = -\omega^2 T.</math>
Each of these is an eigenvalue equation with eigenvalues <math display="inline">-\frac{\omega^2}{c^2}</math> and Template:Math, respectively. For any values of Template:Mvar and Template:Mvar, the equations are satisfied by the functions <math display="block">X(x) = \sin\left(\frac{\omega x}{c} + \varphi\right), \qquad T(t) = \sin(\omega t + \psi),</math> where the phase angles Template:Mvar and Template:Mvar are arbitrary real constants.
If we impose boundary conditions, for example that the ends of the string are fixed at Template:Math and Template:Math, namely Template:Math, and that Template:Math, we constrain the eigenvalues. For these boundary conditions, Template:Math and Template:Math, so the phase angles Template:Math, and <math display="block" alt="sine of omega divided by c quantity equals 0">\sin\left(\frac{\omega L}{c}\right) = 0.</math>
This last boundary condition constrains Template:Mvar to take a value Template:Math, where Template:Mvar is any integer. Thus, the clamped string supports a family of standing waves of the form <math display="block">h(x,t) = \sin\left(\frac{n\pi x}{L} \right) \sin(\omega_n t).</math>
In the example of a string instrument, the frequency Template:Math is the frequency of the Template:Mvar-th harmonic, which is called the Template:Math-th overtone.
Schrödinger equationEdit
In quantum mechanics, the Schrödinger equation <math display="block">i \hbar \frac{\partial}{\partial t}\Psi(\mathbf{r},t) = H \Psi(\mathbf{r},t)</math> with the Hamiltonian operator <math display="block"> H = -\frac{\hbar^2}{2m}\nabla^2+ V(\mathbf{r},t)</math> can be solved by separation of variables if the Hamiltonian does not depend explicitly on time.Template:Sfn In that case, the wave function Template:Math leads to the two differential equations, Template:NumBlk Template:NumBlk Both of these differential equations are eigenvalue equations with eigenvalue Template:Mvar. As shown in an earlier example, the solution of Equation Template:EqNote is the exponential <math display="block"> T(t) = e^{{-iEt}/{\hbar}}.</math>
Equation Template:EqNote is the time-independent Schrödinger equation. The eigenfunctions Template:Mvar of the Hamiltonian operator are stationary states of the quantum mechanical system, each with a corresponding energy Template:Mvar. They represent allowable energy states of the system and may be constrained by boundary conditions.
The Hamiltonian operator Template:Mvar is an example of a Hermitian operator whose eigenfunctions form an orthonormal basis. When the Hamiltonian does not depend explicitly on time, general solutions of the Schrödinger equation are linear combinations of the stationary states multiplied by the oscillatory Template:Math,Template:Sfn <math display="inline"> \Psi(\mathbf{r},t) = \sum_k c_k \varphi_k(\mathbf{r}) e^{{-iE_kt}/{\hbar}} </math> or, for a system with a continuous spectrum, <math display="block"> \Psi(\mathbf{r},t) = \int dE \, c_E \varphi_E(\mathbf{r}) e^{{-iEt}/{\hbar}}.</math>
The success of the Schrödinger equation in explaining the spectral characteristics of hydrogen is considered one of the greatest triumphs of 20th century physics.
Signals and systemsEdit
In the study of signals and systems, an eigenfunction of a system is a signal Template:Math that, when input into the system, produces a response Template:Math, where Template:Mvar is a complex scalar eigenvalue.Template:Sfn
See alsoEdit
- Eigenvalues and eigenvectors
- Hilbert–Schmidt theorem
- Spectral theory of ordinary differential equations
- Fixed point combinator
- Fourier transform eigenfunctions
NotesEdit
CitationsEdit
Works citedEdit
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- Template:Cite book
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- {{#invoke:citation/CS1|citation
|CitationClass=web }} Template:Refend
External linksEdit
- More images (non-GPL) at Atom in a Box