一种求解稀疏信号重构的新算法

New Algorithm for Sparse Signals Reconstruction

由于允许从少量数据中恢复原始信号的压缩感知的引入,基于1范数正则化的最优化方法近来越来越受到重视。利用最小二乘问题的一种等价形式和Bregman迭代方法的一些技巧,本文推导出了可以用于稀疏信号重构求解的非满秩情况下的A+线性Bregman迭代方法的一种新的等价形式,并证明了它与原形式的等价性。

The class of l1 norm regularization problems has received much attention recently because of the introduction of ＂compressed sensing＂ which allows signals to be reconstructed from small amounts of data. With an equivalent form of least squares problem and some techniques of Bregman iterative methods, we induced a derivation of A＋ linear Bregman iteration method that is equivalent to the one that exits.