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Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems
AbstractWe propose the residual expansion (RE) algorithm: a global (or near-global) optimization method for nonconvex least squares problems. Unlike most existing nonconvex optimization techniques, the RE algorithm is not based on either stochastic or multi-point searches; therefore, it can achieve fast global optimization. Moreover, the RE algorithm is easy to implement and successful in high-dimensional optimization. The RE algorithm exhibits excellent empirical performance in terms of k-means clustering, point-set registration, optimized product quantization, and blind image deblurring.
Related Material
[pdf] [arXiv][bibtex]@InProceedings{Ikami_2017_CVPR,
author = {Ikami, Daiki and Yamasaki, Toshihiko and Aizawa, Kiyoharu},
title = {Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}
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