@article{ByrdHribNoce00:siopt,
 author         = {R. H. Byrd and M. E. Hribar and J. Nocedal},
 title          = {An Interior Point Algorithm for Large Scale Nonlinear
                   Programming},
 journal        = SIOPT,
 volume         = 9, number = 4, pages = {877-900}, year = 1999,
 abstract       = {We describe a new algorithm for solving large nonlinear
                   programming problems. It incorporates within the interior
                   point method two powerful tools for solving nonlinear
                   problems: sequential quadratic programming (SQP) and trust
                   region techniques. SQP ideas are used to efficiently handle
                   nonlinearities in the constraints. Trust region strategies
                   allow the algorithm to treat convex and non-convex problems
                   uniformly, permit the direct use of second derivative
                   information and provide a safeguard in the presence of
                   nearly dependent constraint gradients. Both primal and
                   primal-dual versions of the algorithm are developed, and
                   their performance is compared with that of LANCELOT on a
                   set of large and difficult nonlinear problems.},
 summary        = {An algorithm for solving large nonlinear programming
                   problems is described. It incorporates
                   SQP and trust-region techniques within the
                   interior-point method. SQP ideas are used to efficiently
                   handle nonlinearities in the constraints. Trust-region
                   strategies allow the algorithm to treat convex and
                   non-convex problems uniformly, permit the direct use of
                   second derivative information and provide a safeguard in
                   the presence of nearly dependent constraint gradients. Both
                   primal and primal-dual versions of the algorithm are
                   developed, and their performance is compared with that of
                   {\sf LANCELOT} on a set of large and difficult nonlinear
                   problems.}}