Back NLPLSQ: A Fortran Implementation of an SQP-Gauss-Newton Algorithm for Least-Squares Optimization -
User's Guide

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 programming subproblem,
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