A Levenberg-Marquardt method for large-scale bound-constrained nonlinear least-squares
The well known Levenberg-Marquardt method is used extensively for solving nonlinear least-squares problems. We describe an extension of the Levenberg- Marquardt method to problems with bound constraints on the variables. Each iteration of our algorithm approximately solves a linear least-squares pro...
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| Format: | Others |
| Language: | English |
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University of British Columbia
2009
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| Online Access: | http://hdl.handle.net/2429/5648 |
| Summary: | The well known Levenberg-Marquardt method is used extensively for solving
nonlinear least-squares problems. We describe an extension of the Levenberg-
Marquardt method to problems with bound constraints on the variables. Each
iteration of our algorithm approximately solves a linear least-squares problem
subject to the original bound constraints. Our approach is especially suited to
large-scale problems whose functions are expensive to compute; only matrix-vector
products with the Jacobian are required. We present the results of numerical
experiments that illustrate the effectiveness of the approach. Moreover,
we describe its application to a practical curve fitting problem in fluorescence
optical imaging. === Science, Faculty of === Computer Science, Department of === Graduate |
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