NLPLSQ: A Fortran Implementation of an SQP-Gauss-Newton Algorithm for
Least-Squares Optimization -
K. Schittkowski, Report, Department of Computer Science, University of Bayreuth (2007)
The Fortran subroutine NLPLSQ solves
constrained least squares nonlinear programming problems. It is assumed that all
individual problem functions are continuously differentiable. By introducing
additional variables and nonlinear equality constraints, the problem is
transformed into a general smooth nonlinear program subsequently solved by the
sequential quadratic programming (SQP) code NLPQLP.
It can be shown that typical features of special purpose algorithms are
retained, i.e., a combination of a Gauss-Newton and a quasi-Newton search
direction. The additionally introduced variables are eliminated in the quadratic
so that calculation time is not increased significantly. Some comparative numerical results are included, the usage of the code is documented, and an illustrative example is presented.
To download the report, click here: nlplsq.pdf