Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems

Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa

CVPR 2017

doi:10.1109/CVPR.2017.762 /cvpr/2017/ikami2017cvpr-residual/

Abstract

We 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.

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Cite

Text

Ikami et al. "Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems." Conference on Computer Vision and Pattern Recognition, 2017. doi:10.1109/CVPR.2017.762

Markdown

[Ikami et al. "Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems." Conference on Computer Vision and Pattern Recognition, 2017.](https://mlanthology.org/cvpr/2017/ikami2017cvpr-residual/) doi:10.1109/CVPR.2017.762

BibTeX

@inproceedings{ikami2017cvpr-residual,
  title     = {{Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems}},
  author    = {Ikami, Daiki and Yamasaki, Toshihiko and Aizawa, Kiyoharu},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2017},
  doi       = {10.1109/CVPR.2017.762},
  url       = {https://mlanthology.org/cvpr/2017/ikami2017cvpr-residual/}
}