论文标题
有效的最小残留贪婪算法，用于稀疏恢复
Efficient Least Residual Greedy Algorithms for Sparse Recovery
论文作者
论文摘要
我们提出了一种新颖的舞台策略，用于改善贪婪算法以稀疏恢复。我们证明了它在合成和分析稀疏先验方面的效率，在这两种情况下，我们都证明了其计算效率和竞争性重建精度。在综合情况下，我们还为信号恢复提供了理论保证，该信号恢复与现有的基于放松的求解器和其他复杂的贪婪算法的现有完美重建范围相当。
We present a novel stagewise strategy for improving greedy algorithms for sparse recovery. We demonstrate its efficiency both for synthesis and analysis sparse priors, where in both cases we demonstrate its computational efficiency and competitive reconstruction accuracy. In the synthesis case, we also provide theoretical guarantees for the signal recovery that are on par with the existing perfect reconstruction bounds for the relaxation-based solvers and other sophisticated greedy algorithms.
