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A Levenberg-Marquardt method for large-scale bound-constrained nonlinear least-squares

University of British Columbia

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.

Thesis/Dissertation

application/pdf

2009-03-06

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/5648

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/