p = poly(A)
p = poly(r)
A
is an n
-by-n
matrix, poly(A)
is an n+1
element row vector whose elements are the coefficients of the characteristic polynomial, det(sI - A). The coefficients are ordered in descending powers: if a vector c
has n+1
components, the polynomial it represents is
If r
is a column vector containing the roots of a polynomial, poly(r)
returns a row vector whose elements are the coefficients of the polynomial.
If c
is a row vector containing the coefficients of a polynomial, roots(c)
is a column vector whose elements are the roots of the polynomial.
For vectors, roots
and poly
are inverse functions of each other, up to ordering, scaling, and roundoff error.
is returned in a row vector byA =
1 2 3
4 5 6
7 8 0
poly
:
The roots of this polynomial (eigenvalues of matrixp = poly(A)
p =
1 -6 -72 -27
A)
are returned in a column vector by roots
:
r = roots(p)
r =
12.1229
-5.7345
-0.3884
poly
reassembles these into the coefficients of a polynomial:
p2 = poly(r)
p2 =
1 -6 -72 -27
poly
and roots
illustrate an interesting aspect of the modern approach to eigenvalue computation. poly(A)
generates the characteristic polynomial of A
and roots(poly(A))
finds the roots of that polynomial, which are the eigenvalues of A
. But both poly
and roots
use EISPACK eigenvalue subroutines, which are based on similarity transformations. The classical approach, which characterizes eigenvalues as roots of the characteristic polynomial, is actually reversed.
If A
is an n
-by-n
matrix, poly(A)
produces the coefficients c(1)
through c(n+1)
, with c(1) = 1
, in
The algorithm is expressed in an M-file:
This recursion is easily derived by expanding the product.z = eig(A);
c = zeros(n+1,1); c(1) = 1;
for j = 1:n
c(2:j+1) = c(2:j+1)-z(j)
*c(1:j);
end
It is possible to prove that poly(A)
produces the coefficients in the characteristic polynomial of a matrix within roundoff error of A
. This is true even if the eigenvalues of A
are badly conditioned. The traditional algorithms for obtaining the characteristic polynomial, which do not use the eigenvalues, do not have such satisfactory numerical properties.
conv
,polyval
,residue
,roots
(c) Copyright 1994 by The MathWorks, Inc.