An Interior Point Algorithm for Large Scale Nonlinear Programming
Richard Byrd, Mary Beth Hribar, and Jorge Nocedal
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.
Contact: [email protected],
[email protected]
Report OTC 97/05 Optimization Technology
Center, July, 1997.
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