Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems
Residual Expansion Algorithm: Fast and Effective Optimization for Nonconvex Least Squares Problems
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DOI：
10.1109/cvpr.2017.762
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发表时间：
2017-05
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通讯作者：
Daiki Ikami;T. Yamasaki;K. Aizawa
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作者：
Daiki Ikami;T. Yamasaki;K. Aizawa
我们提出了残差展开（RE）算法：一种非凸最小二乘问题的全局（或近全局）优化方法。与大多数现有的非凸优化技术不同，正则化算法既不基于随机搜索，也不基于多点搜索，因此可以实现快速的全局优化。此外，该算法易于实现，在高维优化中取得了成功。该算法在k-means聚类、点集配准、优化产品量化和图像去模糊等方面表现出优异的经验性能。
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.