In the core of the study of the model that describes an LSS we find
the quadratic eigenproblem (QE) *Kz = lambda^2 Mz + lambda Cz* of size
*n*. The most popular technique to date, for the QE, first transforms
it to a linear eigenproblem (LE) *Ax = lambda Bx*, of size *2n* and
then solves the LE, with obvious disadvantages. Our main objective is to
develop algorithms for the QE that will operate on size *n* data instead.
We intend to use techniques, in the spirit of the *QZ* algorithm, such as
* deflation, implicit shifts* and * double steps*.

* Revised: 1994.10.13*