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Matrix polynomial : ウィキペディア英語版
Matrix polynomial

In mathematics, a matrix polynomial is a polynomial with matrices as variables. Given an ordinary, scalar-valued polynomial
: P(x) = \sum_^n =a_0 + a_1 x+ a_2 x^2 + \cdots + a_n x^n,
this polynomial evaluated at a matrix ''A'' is
:P(A) = \sum_^n =a_0 I + a_1 A + a_2 A^2 + \cdots + a_n A^n,
where ''I'' is the identity matrix.
A matrix polynomial equation is an equality between two matrix polynomials, which holds for the specific matrices in question. A matrix polynomial identity is a matrix polynomial equation which holds for all matrices ''A'' in a specified matrix ring ''Mn''(''R'').
== Characteristic and minimal polynomial ==

The characteristic polynomial of a matrix ''A'' is a scalar-valued polynomial, defined by p_A(t) = \det \left(tI - A\right). The Cayley–Hamilton theorem states that if this polynomial is viewed as a matrix polynomial and evaluated at the matrix ''A'' itself, the result is the zero matrix: p_A(A) = 0. The characteristic polynomial is thus a polynomial which annihilates ''A''.
There is a unique monic polynomial of minimal degree which annihilates ''A''; this polynomial is the minimal polynomial. Any polynomial which annihilates ''A'' (such as the characteristic polynomial) is a multiple of the minimal polynomial.
It follows that that given two polynomials ''P'' and ''Q'', we have P(A) = Q(A) if and only if
: P^(\lambda_i) = Q^(\lambda_i) \qquad \text j = 0,\ldots,n_i \text i = 1,\ldots,s,
where P^ denotes the ''j''th derivative of ''P'' and \lambda_1, \dots, \lambda_s are the eigenvalues of ''A'' with corresponding indices n_1, \dots, n_s (the index of an eigenvalue is the size of its largest Jordan block).

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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