NLPIP: A Fortran Implementation of an SQP Interior Point Algorithm for Solving Large Scale Nonlinear Optimization Problems- User's Guide, Version 2.0

B. Sachsenberg, Report, Department of Computer Science, University of Bayreuth (2013)
Abstract: The Fortran subroutine NLPIP solves solves large nonlinear programming problems with equality and inequality constraints, i.e., problems with a large number of variables. It is implicitly assumed that the Jacobian matrix of the constraints is sparse. Problem functions must be continuously differentiable. The underlying algorithm applies is a combined SQP-IPM strategy. Depending on the preferences of the user, either a standard SQP method is used where the quadratic programming subproblem is solved by an interior point method, or a nonlinear interior point method is executed. Moreover, any combination in between is possible. BFGS updates use the limited memory method, and three different merit functions are available. In any case, the primal-dual system of linear equations possesses the same structure and must be solved by a user-provided routine depending on the sparsity patterns of the Jacobian matrix of the constraints. Numerical results are included based on a set of small, dense problems, elliptic control problems, and large-scale cuter-problems.

To download the report, click here: NLPIP.pdf


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