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 real scalar c. Recall that a Hermitian (or real symmetric) matrix has 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 to obtain an eigenvalue approximation from an eigenvector approximation. Specifically, this is the basis for Rayleigh quotient iteration.
The range of the Rayleigh quotient (for matrix that is 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.
Special case of covariance matrices
An empirical covariance matrix M can be represented as the product A' A of the data matrix A pre-multiplied by its transpose A'. Being a symmetrical real matrix, M 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 vi 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 maximised by the eigenvector with the largest eigenvalue, consider decomposing an arbitrary vector x on the basis of the eigenvectors vi:
is the coordinate of x orthogonally projected onto vi. Therefore we have:
which, by orthogonality 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 x and each eigenvector vi, weighted by corresponding eigenvalues.
If a vector x maximizes , then any non-zero scalar multiple kx also maximizes R, so the problem can be reduced to the Lagrange problem of maximizing under the constraint that .
Define: βi = α2
i. 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 i > 1 (when the eigenvalues are ordered by decreasing magnitude).
Thus, as advertised, the Rayleigh quotient is maximised by the eigenvector with the largest eigenvalue.
Formulation using Lagrange multipliers
subject to the constraint I.e. to find the critical points of
where λ is a Lagrange multiplier. The stationary points of occur at
Therefore, the eigenvectors of M are the critical points of the Rayleigh Quotient and their corresponding eigenvalues are the stationary values of R.
Use in Sturm–Liouville theory
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:
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.
- 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
- 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.