Algorithms for Nonlinear Least-Squares Problems
Abstract
This paper addresses a nonlinear least-squares problem which arises most often in data fitting applications. Much research has focused on the development of specialized algorithms that attempt to exploit the structure of the nonlinear least-squares objective. We survey methods developed for problems in which sparsity in the derivatives of f is not taken into account in formulating algorithms. Keywords: Gauss-Neutron methods, Levenberg-Marquardt methods; Quasi-Newton methods; Quadratic programming; Optimization.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 1988
- Accession Number
- ADA196071
Entities
People
- Christina Fraley
Organizations
- Stanford University