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

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

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

2019-12-06 |

161 |

115 |

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

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 highdimensional 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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