NLPQL
| Author | K. Schittkowski
University of Bayreuth Faculty of Mathmatics and Physics D-95440 Bayreuth Germany |

Language | FORTRAN-77 | |

Algorithm | NLPQL solves general nonlinear mathematical programming problems with equality and inequality constraints. It is assumed that all problem functions are continuously 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 by the dual code QL. Subsequently a line search is performed with respect to two alternative merit functions and the Hessian approximation is updated by the modified BFGS-formula. | |

Input Format | ||

Modeling Languages link | ||

Commercial Status | For more details contact the author.
E-mail: klaus.schittkowski@uni-bayreuth.de | |

Platform | Any machine with a reasonable amount of memory and a Fortran compiler. | |

Remarks | NLPQL is written in double precision FORTRAN-77 and organized in form of a subroutine. Nonlinear problem functions and analytical gradients must be provided by the user within special subroutines or the calling program.
Special Features: - upper and lower bounds on the variables handled separately,
- reverse communication (evaluation of function values in main program),
- scaling of function values,
- initial multiplier and Hessian approximation,
- bounds and linear constraints remain satisfied,
- full documentation by initial comments,
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References | K. Schittkowski, NLPQL: A Fortran subroutine for solving constrained nonlinear programming problems, Annals of Operations Research, Vol. 5, 485-500 (1985/86) |