ABSTRACT
This article is concerned with algorithmic aspects of nonlinear least squares model fitting problems. Such problems differ from general optimization problems due to the special structure of the least squares normal equations. In simple least squares problems, that structure allows one to partition the normal equation system, whereby significant algorithmic simplification can be achieved. The more general least squares problems, considered here, often can be made partitionable by proper parameter manipulations. In this article, sufficient partitionability conditions are derived and parameter manipulations are discussed for the achievement of partitionability.
