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\documentclass{article}
\usepackage{axiom}
\begin{document}
\title{\$SPAD/src/input eigen.input}
\author{The Axiom Team}
\maketitle
\begin{abstract}
\end{abstract}
\eject
\tableofcontents
\eject
\section{License}
<<license>>=
--Copyright The Numerical Algorithms Group Limited 1991.
@
<<*>>=
<<license>>
-- Computation of eigenvalues and eigenvectors
)cl all
-- computation of characteristic polynomial
-- for matrix of integers
m:=matrix([[1,2,1],[2,1,-2],[1,-2,4]])
characteristicPolynomial m
characteristicPolynomial(m,x)
-- for matrix of polynomials
p:=matrix([[x+1,2-x*y,x**2+1],[2-x,y+2*x,x**2-2],[y**2,x-2,4-x*y]])
characteristicPolynomial p
characteristicPolynomial(p,t)
-- for general matrix of FRAC POLY INT
n:=matrix([[a,b,c],[d,e,f],[g,h,k]])
characteristicPolynomial n
-- there are many functions for the computation of eigenvalues and
-- eigenvectors:
-- the function eigenvalues returns the eigenvalues :
leig := eigenvalues m
alpha:=leig.1
-- alpha is a rational eigenvalue; the corresponding eigenvector can be
-- computed with the function eigenvector:
eigenvector(alpha,m)
beta:=leig.2
-- beta is a "symbolic" eigenvalue, i.e. it is a root (symbolically expressed)
-- of an irreducible factor of the characteristic polynomial. In this case
-- too we can compute the associate eigenvectors.
eigenvector(beta,m)$EP(INT)
-- eigenvector(beta,m) not accepted by the interpreter
-- eigenvalues and eigenvectors can be computed simultaneously
-- with the function eigenvectors
eigenvectors m
q:=matrix [[x**2-y**2,(x-y)*(2*x+3*y)],[x+y,2*x+3*y]]
eigenvectors(q)
p:=matrix([[76,-18,58,-10],[-4,78,2,-2],[-6,15,45,3],[22,-75,7,41]])
ll := eigenvectors p
-- In this case the algebraic multiplicity (the field eigmult) is different
-- from the geometric multiplicity (the length of the field eigvec).
generalizedEigenvectors p
generalizedEigenvector(ll.1,p)$EP(INT)
-- generalizedEigenvector(ll.1,p) the interpreter can not handle this
-- these functions return respectively the complete set of
-- generalized eigenvectors
-- or the generalized eigenvectors associated to a particular eigenvalue alpha,
-- i.e. a basis of the nullSpace((p-alpha*I)**k) where k is the algebraic
-- multiplicity of alpha.
-- In the case of symbolic eigenvalues it is possible to convert the symbolic
-- eigenvalue and the corresponding eigenvectors in a more explicit form.
m
mm:=matrix([[30,4,24],[-17,8,-7],[-31,-54,-5]])
-- the function radicalEigenvalues expresses, when possible, the eigenvalues
-- in terms of radicals.
le1:=radicalEigenvalues m
le2:=radicalEigenvalues mm
-- the function radicalEigenvector computes the eigenvectors assocoted to
-- a given eigenvalue, expressed in terms of radicals
radicalEigenvector(le1.2, m)
radicalEigenvector(le2.2,mm)
-- the function radicalEigenvectors computes simoultaneously all the
-- eigenvalues and the associated eigenvectors and expresses the , when
-- it is possible, in terms of radical.
radicalEigenvectors m
radicalEigenvectors mm
-- there exist analogous functions for the computation of real and complex
-- eigenvalues and eigenvectors.
-- in order to compute respectively the eigenvalues or the
-- eigenvalues and the associared eigevectors of a matrix m and express them
-- as rational numbers (Gaussian rational) up to precision 1/1000000 use
realEigenvalues(m,1/1000000)
complexEigenvalues(mm,1/1000000)
realEigenvectors(m,1/1000000)
complexEigenvectors(mm,1/1000000)
-- to have the eigenvalues expressed as real float (complex float) use
realEigenvalues(m,.000001)
realEigenvectors(m,.000001)
complexEigenvalues(mm,.000001)
complexEigenvectors(mm,.000001)
@
\eject
\begin{thebibliography}{99}
\bibitem{1} nothing
\end{thebibliography}
\end{document}
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