A New Nonconvex Sparse Recovery Method for Compressive Sensing

Frontiers in Applied Mathematics and Statistics

关键词: compressive sensing;    nonconvex sparse recovery;    iteratively reweighted least squares;    difference of convex functions;    q-ratio constrained minimal singular values;   

DOI  :  10.3389/fams.2019.00014

来源: DOAJ

【 摘 要 】

As an extension of the widely used ℓr-minimization with 0 < r ≤ 1, a new non-convex weighted ℓr − ℓ1 minimization method is proposed for compressive sensing. The theoretical recovery results based on restricted isometry property and q-ratio constrained minimal singular values are established. An algorithm that integrates the iteratively reweighted least squares algorithm and the difference of convex functions algorithm is given to approximately solve this non-convex problem. Numerical experiments are presented to illustrate our results.

【 授权许可】

Unknown