Rayleigh quotient
In mathematics, for a given complex Hermitian matrix M and nonzero vector x, the Rayleigh quotient^{[1]} , is defined as:^{[2]}^{[3]}
For real matrices and vectors, the condition of being Hermitian reduces to that of being symmetric, and the conjugate transpose to the usual transpose . Note that for any non-zero scalar c. Recall that a Hermitian (or real symmetric) matrix is diagonalizable with only real eigenvalues. It can be shown that, for a given matrix, the Rayleigh quotient reaches its minimum value (the smallest eigenvalue of M) when x is (the corresponding eigenvector). Similarly, and .
The Rayleigh quotient is used in the min-max theorem to get exact values of all eigenvalues. It is also used in eigenvalue algorithms (such as Rayleigh quotient iteration) to obtain an eigenvalue approximation from an eigenvector approximation.
The range of the Rayleigh quotient (for any matrix, not necessarily Hermitian) is called a numerical range (or spectrum in functional analysis). When the matrix is Hermitian, the numerical range is equal to the spectral norm. Still in functional analysis, is known as the spectral radius. In the context of C*-algebras or algebraic quantum mechanics, the function that to M associates the Rayleigh–Ritz quotient R(M,x) for a fixed x and M varying through the algebra would be referred to as "vector state" of the algebra.
In quantum mechanics, the Rayleigh quotient gives the expectation value of the observable corresponding to the operator M for a system whose state is given by x.
If we fix the complex matrix M, then the resulting Rayleigh quotient map (considered as a function of x) completely determines M via the polarization identity; indeed, this remains true even if we allow M to be non-Hermitian. (However, if we restrict the field of scalars to the real numbers, then the Rayleigh quotient only determines the symmetric part of M.)
Contents
Bounds for Hermitian
As stated in the introduction, for any vector x, one has , where are respectively the smallest and largest eigenvalues of . This is immediately after observing that the Rayleigh quotient is a weighted average of eigenvalues of M:
where is the th eigenpair after orthonormalization and is the th coordinate of x in the eigenbasis. It is then easy to verify that the bounds are attained at the corresponding eigenvectors .
The fact that the quotient is a weighted average of the eigenvalues can be used to identify the second, the third, ... largest eigenvalues. Let be the eigenvalues in decreasing order. If and is constrained to be orthogonal to , in which case , then has maximum value , which is achieved when .
Special case of covariance matrices
An empirical covariance matrix can be represented as the product of the data matrix pre-multiplied by its transpose . Being a positive semi-definite matrix, has non-negative eigenvalues, and orthogonal (or othogonalisable) eigenvectors, which can be demonstrated as follows.
Firstly, that the eigenvalues are non-negative:
Secondly, that the eigenvectors are orthogonal to one another:
if the eigenvalues are different – in the case of multiplicity, the basis can be orthogonalized.
To now establish that the Rayleigh quotient is maximized by the eigenvector with the largest eigenvalue, consider decomposing an arbitrary vector on the basis of the eigenvectors :
where
is the coordinate of orthogonally projected onto . Therefore, we have:
which, by orthonormality of the eigenvectors, becomes:
The last representation establishes that the Rayleigh quotient is the sum of the squared cosines of the angles formed by the vector and each eigenvector , weighted by corresponding eigenvalues.
If a vector maximizes , then any non-zero scalar multiple also maximizes , so the problem can be reduced to the Lagrange problem of maximizing under the constraint that .
Define: . This then becomes a linear program, which always attains its maximum at one of the corners of the domain. A maximum point will have and for all (when the eigenvalues are ordered by decreasing magnitude).
Thus, the Rayleigh quotient is maximized by the eigenvector with the largest eigenvalue.
Formulation using Lagrange multipliers
Alternatively, this result can be arrived at by the method of Lagrange multipliers. The first part is to show that the quotient is constant under scaling , where is a scalar
Because of this invariance, it is sufficient to study the special case . The problem is then to find the critical points of the function
- ,
subject to the constraint In other words, it is to find the critical points of
where is a Lagrange multiplier. The stationary points of occur at
and
Therefore, the eigenvectors of are the critical points of the Rayleigh quotient and their corresponding eigenvalues are the stationary values of . This property is the basis for principal components analysis and canonical correlation.
Use in Sturm–Liouville theory
Sturm–Liouville theory concerns the action of the linear operator
on the inner product space defined by
of functions satisfying some specified boundary conditions at a and b. In this case the Rayleigh quotient is
This is sometimes presented in an equivalent form, obtained by separating the integral in the numerator and using integration by parts:
Generalizations
- For a given pair (A, B) of matrices, and a given non-zero vector x, the generalized Rayleigh quotient is defined as:
- The Generalized Rayleigh Quotient can be reduced to the Rayleigh Quotient through the transformation where is the Cholesky decomposition of the Hermitian positive-definite matrix B.
- For a given pair (x, y) of non-zero vectors, and a given Hermitian matrix H, the generalized Rayleigh quotient can be defined as:
- which coincides with R(H,x) when x = y. In quantum mechanics, this quantity is called a "matrix element" or sometimes a "transition amplitude".
See also
References
- ^ Also known as the Rayleigh–Ritz ratio; named after Walther Ritz and Lord Rayleigh.
- ^ Horn, R. A. and C. A. Johnson. 1985. Matrix Analysis. Cambridge University Press. pp. 176–180.
- ^ Parlet B. N. The symmetric eigenvalue problem, SIAM, Classics in Applied Mathematics,1998
Further reading
- Shi Yu, Léon-Charles Tranchevent, Bart Moor, Yves Moreau, Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining, Ch. 2, Springer, 2011.