Title :
A Necessary and Sufficient Condition for Generalized Demixing
Author :
Chun-Yen Kuo ; Gang-Xuan Lin ; Chun-Shien Lu
Author_Institution :
Inst. of Inf. Sci., Taipei, Taiwan
Abstract :
Demixing is the problem of identifying multiple structured signals from a superimposed observation. This work analyzes a general framework, based on convex optimization, for solving demixing problems. We present a new solution to determine whether or not a specific convex optimization problem built for generalized demixing is successful. This solution also creates a way to estimate the probability of success by the approximate kinematic formula.
Keywords :
compressed sensing; convex programming; probability; signal denoising; approximate kinematic formula; compressive sensing; convex optimization problem; generalized demixing problem; multiple structured signal identification; probability; Compressed sensing; Convex functions; Economic indicators; Kinematics; Null space; Optimization; Standards; ${ell _1}$-minimization; Compressive sensing; conic geometry; convex optimization; sparse signal recovery;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2015.2457403
