Algorithms for nonlinear least-squares problems
Technical Report
·
OSTI ID:6727845
This paper addresses the nonlinear least-squares problem min/sub xepsilon/Re//sup n///parallel/f(x)/parallel//sub 2//sup 2/, where f(x) is a vector in /Re//sup m/ whose components are smooth nonlinear functions. The problem 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 numerical methods developed for problems in which sparsity in the derivatives of f is not taken into account in formulating algorithms. 85 refs.
- Research Organization:
- Stanford Univ., CA (USA). Systems Optimization Lab.
- DOE Contract Number:
- FG03-87ER25030
- OSTI ID:
- 6727845
- Report Number(s):
- SOL-88-16; ON: DE89001769
- Country of Publication:
- United States
- Language:
- English
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