Large-scale nonlinear constrained optimization:
                 a current survey

      A.R. Conn, Nick Gould and Ph.L. Toint

                  Report 94/01

Much  progress has  been made in constrained nonlinear
optimization   in  the  past  ten   years,   but  most
large-scale problems  still  represent  a considerable
obstacle.

In  this  survey  paper we  will  attempt to  give  an
overview of the current approaches, including interior
and exterior methods and  algorithms  based upon trust
regions   and  line  searches.    In   addition,   the
importance of  software,  numerical linear algebra and
testing will be  addressed. We will try to explain why
the difficulties arise, how attempts are being made to
overcome them and  some  of  the  problems that  still
remain.

Although there will be some  emphasis  on the LANCELOT
and  CUTE  projects, the intention is to give a  broad
picture of the state-of-the-art.