Title :
Compressed sensing and r-algorithms
Author :
Glazunov, Nikolaj
Author_Institution :
Dept. of Software Eng., Nat. Aviation Univ., Kiev, Ukraine
Abstract :
This paper concerns the author´s research in progress. It is not a paper of a completed work. We have a principal example, nuclear norm optimization problem, which is described intuitively and formally. We believe that the proper formulation (and implementation) of r-algorithm based solution of the problem will have general applications and we are still seeking that formulation (and implementation). It lies somewhere in the neighborhood of a matrix extension of r-algorithms. In the framework we consider approaches and problems of compressed sensing, review new results in the field and investigate applications of r-algorithms and their modifications to solution of the problem recovering the data matrix from a sampling of its elements.
Keywords :
convex programming; data compression; matrix algebra; signal reconstruction; signal sampling; compressed sensing; convex optimization; data matrix recovery problem; nuclear norm optimization problem; r-algorithms; Coherence; Compressed sensing; Convex functions; Linear matrix inequalities; Matrix decomposition; Optimization; Programming; Compressed sensing; convex optimization; r-algorithm; singular value decomposition; subgradient;
Conference_Titel :
Microwaves, Radar and Remote Sensing Symposium (MRRS), 2011
Conference_Location :
Kiev
Print_ISBN :
978-1-4244-9641-9
DOI :
10.1109/MRRS.2011.6053620