Rayleigh quotient

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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 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.

Bounds for Hermitian [edit]

As stated in the introduction, it is . This is immediate 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 is constrained to be orthogonal to , in which case , then has the maximum , which is achieved when .

Special case of covariance matrices[edit]

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 :


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, as advertised, the Rayleigh quotient is maximized by the eigenvector with the largest eigenvalue.

Formulation using Lagrange multipliers[edit]

Alternatively, this result can be arrived at by the method of Lagrange multipliers. The problem is 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


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[edit]

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:


  1. 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.
  2. 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.

See also[edit]


  1. ^ Also known as the Rayleigh–Ritz ratio; named after Walther Ritz and Lord Rayleigh.
  2. ^ Horn, R. A. and C. A. Johnson. 1985. Matrix Analysis. Cambridge University Press. pp. 176–180.
  3. ^ Parlet B. N. The symmetric eigenvalue problem, SIAM, Classics in Applied Mathematics,1998

Further reading[edit]