Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems
Computer Vision and Pattern Recognition
2017-05-29 v1
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.
Cite
@article{arxiv.1705.09549,
title = {Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems},
author = {Daiki Ikami and Toshihiko Yamasaki and Kiyoharu Aizawa},
journal= {arXiv preprint arXiv:1705.09549},
year = {2017}
}
Comments
Accepted to CVPR2017