NLPQL

Smooth nonlinear programming with equality and inequality constraints

NLPQL solves smooth nonlinear programming problems, i.e. minimizes a nonlinear objective function subject to nonlinear equality and inequality constraints. It is assumed that all model functions are contiuously differentiable.

The internal algorithm is a sequential quadratic programming (SQP) method. Proceeding from a quadratic approximation of the Lagrangian function and a linearization of the constraints, a quadratic subproblem is formulated and solved to get a search direction. Subsequently a line search is performed with respect to two alternative merit functions, and the Hessian approximation is updated by the modified BFGS formula.

Special features of NLPQL are

separate handling of upper and lower bounds on the variables,
reverse communication,
internal scaling,
initial multiplier and Hessian approximation,
feasibility with respect to bounds and linear constraints,
full documentation by initial comments.

NLPQL is written in double-precision Fortran 77 and organized in the form of a subroutine. Nonlinear problem functions and analytical gradients must be provided by the user within special subroutines or the calling program.

Need more info?

Take a look at the author's home page, or contact

Prof. K. Schittkowski 
Dept. of Mathematics
University of Bayreuth 
95440 Bayreuth, Germany

klaus.schittkowski@uni-bayreuth.de

Reference:

K. Schittkowski, NLPQL: A Fortran subroutine for solving constrained nonlinear programming problems, Annals of Operations Research, Vol. 5, 4850-500 (1985/86).

[ Optimization Software Guide | OTC Home Page | NEOS Server | NEOS Guide ]