[ont.events] Symplectic Eigenvalue Algorithms.

ylfink@water.waterloo.edu (ylfink) (11/16/88)

DEPARTMENT OF COMPUTER SCIENCE
UNIVERSITY OF WATERLOO
SEMINAR ACTIVITIES

SCIENTIFIC COMPUTATION SEMINAR

                    -  Thursday, November 24, 1988

Dr.  Angelika  Bunse-Gerstner,  Universitat  Bielefeld,
West  Germany,  will  speak  on ``Symplectic Eigenvalue
Algorithms''.

TIME:                4:00 PM

ROOM:              DC 1304

ABSTRACT

In  several  areas  of  application  matrix  eigenvalue
problems  Mx = lambda x  arise,  in which the matrix M has a
special  symmetry  structure.  It  can  be described by
symmetries of JM, where

                     J = [ 0    I]
                          -I    0

and  I  is the identity. The symmetry structure implies
that  the  eigenvalues of M occur pairwise and for some
of  these  problems  the  eigenvalue  pairs  have to be
separated.   The  usual  eigenvalue  algorithms for the
computation  of  the  Schur  form  cannot exploit these
structures.  They  treat the eigenvalue problem like an
unstructured  one.  In  particular  because of rounding
errors  they  may  lose  the  pairing  for the computed
eigenvalues.   For  some  of  these problems eigenvalue
algorithms     based     on    symplectic    similarity
transformations  can  be  developed, which preserve the
symmetry  structure  throughout  the process. They need
only  half  the  work  and  storage of the conventional
algorithms  to  compute  a matrix R of a modified Schur
form.   The   eigenvalue  pairing  is  preserved,  more
precisely  the  computed  R  is exactly similar to M+E,
where  E  is  a  matrix with small norm having the same
structure  as M.  In this talk examples of problems are
given which lead to such eigenvalue computations and it
is  shown  how  symplectic eigenvalue algorithms can be

                         - 2 -

developed for these problems.

                   November 16, 1988