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