Nonlinear Programming Packages

NLPSPR Authors Dr. John T. Betts or
Dr. Paul D. Frank
Boeing Computer Services
PO Box 24346, MS 7L-21
Seattle, WA 98124-0346
Language FORTRAN 77
Algorithm Sequential quadratic programming algorithm that uses an augmented Lagrangian merit function and a sparse quadratic programming algorithm based on the Schur complement approach of Gill, Murray, Saunders, and Wright.
Input Format  
Modeling Languages link  
Commercial Status  
The package is dedicated to solve nonlinear programming problems with a large number of variables and constraints where the Jacobian and Hessian matrices are sparse.
Sparse linear systems are solved efficiently using a multifrontal algorithm that implements
a modified Cholesky decomposition for symmetric indefinite systems. The user must supply the sparse Jacobian and Hessian matrices, although this information can be computed efficiently using sparse finite differences which are implemented in a utility package that is also available. The software incorporates a reverse communication structure and is especially well suited for applications derived from discretized optimal control problems.
References J. T. Betts and P. D. Frank, A sparse nonlinear optimization algorithm, Technical Report AMS-TR-173, Applied Mathematics and Statistics Group, Boeing Computer Services, 1991.