Abstract
We report some experience with optimization methods applied to an inverse light scattering problem for spherical, homogeneous particles. Such particles can be identified from experimental data using a least squares global optimization method. However, if there is significant noise in the data, the “best” solution may not correspond well to the “actual” particle. We suggest a way in which the original least squares solution may be improved by using a constrained optimization calculation which considers the position of peaks in the data. This approach is applied first to multi-angle data with varying amounts of artificially introduced noise and then to examples of single-particle experimental data patterns characterized by high noise levels.
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Bartholomew-Biggs, M.C., Ulanowski, Z.J. & Zakovic, S. Using Global Optimization for a Microparticle Identification Problem with Noisy Data. J Glob Optim 32, 325–347 (2005). https://doi.org/10.1007/s10898-004-1943-0
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DOI: https://doi.org/10.1007/s10898-004-1943-0