优化算法汇总

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于 2011-03-29 10:07:00 发布

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本文介绍了几种用于解决线性逆问题的算法，包括基于L1范数最小化的稀疏信号恢复方法，如重加权L1最小化算法、迭代阈值算法及快速迭代收缩阈值算法等，并提到了用于大规模无约束优化问题的序列子空间优化方法。

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interior point

block coordinate relaxation

Boltzmann machine

求解L1范数最小化

E. Candes, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted l1 minimization,” Journalof Fourier Analysis and Applications, vol. 14, pp. 877-905, Dec. 2008.
I. Daubechies, M. Defriese, and C. DeMol, “An iterative thresholding algorithm for linear inverseproblems with a sparsity constraint,” Commun. Pure Appl. Math., vol.57, pp.1413-1457, 2004.（Interative Shrinkage algorithm）

加速算法

G. Narkiss and M. Zibulevsky, “Sequential subspace optimization method for large-scale unconstrainedoptimization,” Technical Report CCIT No. 559, Technion, Israel Institute of Technology, Haifa, 2005.
A. Beck and M. Teboulle, “A fast iterative shrinkage thresholding algorithm for linear inverseproblems,” SIAM J. Imaging Sciences, vol. 2, no. 1:183202, 2009.

转载于:https://www.cnblogs.com/Jren2012/archive/2011/03/29/1998465.html