Nonlinear Programming Packages

NLPQL Author K. Schittkowski
University of Bayreuth
Faculty of Mathmatics and Physics
D-95440 Bayreuth
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.
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,
References K. Schittkowski, NLPQL: A Fortran subroutine for solving constrained nonlinear programming problems, Annals of Operations Research, Vol. 5, 485-500 (1985/86)