2015/01/15 by Hui Zhang, Zhang, Hui · 2 citations
Computer Science · Engineering · Mathematics · #Algorithm #Compressed sensing #Computer science #Convex optimization #Cover (algebra) #Distributed Sensor Networks and Detection Algorithms #Engineering #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Lasso (programming language) #Mathematical optimization #Mathematics #Optimization and Control (math.OC) #Property (philosophy) #Regular polygon #Robust optimization #Robustness (evolution) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #Underdetermined system #cs.IT #math.IT #math.OC
paper · pdf · doi:10.48550/arxiv.1501.03643
published in arXiv (Cornell University) (Cornell University) · 8 pages; A mistake has been corrected
openalex publication_date 2015/01/15 · arxiv created 2016/04/03 · arxiv updated 2016/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently, Cahill and Mixon completely characterized the sensing operators in many compressed sensing instances with a robust width property. The proposed property allows uniformly stable and robust reconstruction of certain solutions from an underdetermined linear system via convex optimization. However, their theory does not cover the Lasso and Dantzig selector models, both of which are popular alternatives in the statistics community. In this letter, we show that the robust width property can be perfectly applied to these two models as well. Our results solve an open problem left by Cahill and Mixon.