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