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Raleigh quotient : ウィキペディア英語版
Rayleigh quotient
In mathematics, for a given complex Hermitian matrix ''M'' and nonzero vector ''x'', the Rayleigh quotient〔Also known as the Rayleigh–Ritz ratio; named after Walther Ritz and Lord Rayleigh.〕 R(M, x), is defined as:〔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〕
:R(M,x) := x}.
For real matrices and vectors, the condition of being Hermitian reduces to that of being symmetric, and the conjugate transpose x^ to the usual transpose x'. Note that R(M, c x) = R(M,x) 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 \lambda_\min (the smallest eigenvalue of ''M'') when ''x'' is v_\min (the corresponding eigenvector). Similarly, R(M, x) \leq \lambda_\max and R(M, v_\max) = \lambda_\max.
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, \lambda_\max 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 M==
As stated in the introduction, it is R(M,x) \in \left(\lambda_\max \right ). This is immediate after observing that the Rayleigh quotient is a weighted average of eigenvalues of ''M'':
:R(M,x) = x} = \frac
where (\lambda_i, v_i) is the ith eigenpair after orthonormalization and y_i = v_i^
* x is the ith coordinate of ''x'' in the eigenbasis. It is then easy to verify that the bounds are attained at the corresponding eigenvectors v_\min, v_\max.
The fact that the quotient is a weighted average of the eigenvalues can be used to identify the second, the third, ... largest eigenvalues. Let \lambda_ = \lambda_1 \ge \lambda_2 \ge ... \ge \lambda_n
= \lambda_
be the eigenvalues in decreasing order. If x is constrained to be orthogonal to v_1, in which case y_1 = v_1^
*x = 0 , then R(M,x) has the maximum \lambda_2, which is achieved when x = v_2.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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