A primal-dual algorithm for minimizing a non-convex function 
           subject to bound and linear equality constraints

           A. R. Conn    N. I. M. Gould    Ph. L. Toint

                            Report 96/7


A new primal-dual algorithm is  proposed for  the  minimization of  non-convex
objective functions subject to simple bounds and linear equality  constraints.
The  method alternates between  a classical primal-dual step and a Newton-like
step in order to ensure descent on a suitable  merit function.  Convergence of
a  well-defined  subsequence  of iterates  is  proved from arbitrary  starting
points. Algorithmic variants are discussed  and  preliminary numerical results
presented.