This report forms the user's guide for Version 4.0 of NPSOL, a set of Fortran subroutines designed to minimize a smooth function subject to constraints, which may include simple bounds on the variables, linear constraints and smooth nonlinear constraints. (NPSOL may also be used for unconstrained, bound-constrained and linearly constrained optimization.) The user must provide subroutines that define the objective and constraint functions and (optionally) their gradients. All matrices are treated as dense, and hence NPSOL is not intended for large sparse problems. NPSOL uses a sequential quadratic programming (SQP) algorithm, in which the search directions is the solution of a quadratic programming (QP) subproblem. The algorithm treats bounds, linear constraints and nonlinear constraints separately. The Hessian of each QP subproblem is a positive-definite quasi-Newton approximation to the Hessian of the Lagrangian function. The steplength at each iteration is required to produce a sufficient decrease an augmented Lagrangian merit function. Each QP subproblem is solved using a quadratic programming package with several features that improve the efficiency of an SQP algorithm. (Author).4. USER-SUPPLIED SUBROUTINES The user must provide subroutines that define the objective function and nonlinear constraints. The objective function is ... OBJGRD is ignored. (Output) If for some reason you wish to terminate the ... only once. If there are nonlinear constraints, the first 12 U$era#39;$ Guide for NPSOL 4-0.
|Title||:||User's guide for NPSOL (version 4.0): a Fortran package for nonlinear programming|
|Author||:||Stanford University. Dept. of Operations Research. Systems Optimization Laboratory, P. E. Gill, Walter Murray, Michael A. Saunders, Margaret H. Wright, United States. Dept. of Energy, National Science Foundation (U.S.), United States. Office of Naval Research, United States. Army Research Office|