论文标题
$ q $ -ratio稀疏的最小化，$ 1 <q \ leq \ infty $用于信号恢复
Minimization of the $q$-ratio sparsity with $1 < q \leq \infty$ for signal recovery
论文作者
论文摘要
在本文中，我们提出了一种通用不变的方法，可以通过最小化$ q $ ratatio稀疏性来稀疏信号恢复。当$ 1 <q \ leq \ infty $时，介绍了基于$ q $ -ratio的最小奇异值（CMSV）和通过非线性分数编程的实用算法的理论分析。进行了数值实验，以证明所提出的方法比最先进的稀疏恢复方法的优势性能。
In this paper, we propose a general scale invariant approach for sparse signal recovery via the minimization of the $q$-ratio sparsity. When $1 < q \leq \infty$, both the theoretical analysis based on $q$-ratio constrained minimal singular values (CMSV) and the practical algorithms via nonlinear fractional programming are presented. Numerical experiments are conducted to demonstrate the advantageous performance of the proposed approaches over the state-of-the-art sparse recovery methods.
