the focus of this paper is to consider the compressed sensing problem. it is stated that the compressed sensing theory, under certain conditions, helps relax the nyquist sampling theory and takes smaller samples. one of the important tasks in this theory is to carefully design measurement matrix (sampling operator). most existing methods in the literature attempt to optimize a randomly initiali...

List of included articles [1] H. Rauhut. Random sampling of sparse trigonometric polynomials. Appl. Comput. [2] S. Kunis and H. Rauhut. Random sampling of sparse trigonometric polynomials II-orthogonal matching pursuit versus basis pursuit. [3] H. Rauhut. Stability results for random sampling of sparse trigonometric polynomi-als. [4] H. Rauhut. On the impossibility of uniform sparse reconstruct...

We give some new results on sparse signal recovery in the presence of noise, for weighted spaces. Traditionally, were used dictionaries that have the norm equal to 1, but, for random dictionaries this condition is rarely satised. Moreover, we give better estimations then the ones given recently by Cai, Wang and Xu.

We consider the sparse recovery problem on Euclidean Jordan algebra (SREJA), which includes sparse signal recovery and low-rank symmetric matrix recovery as special cases. We introduce the restricted isometry property, null space property (NSP), and s-goodness for linear transformations in s-sparse element recovery on Euclidean Jordan algebra (SREJA), all of which provide sufficient conditions ...

we give some new results on sparse signal recovery in the presence of noise, forweighted spaces. traditionally, were used dictionaries that have the norm equal to 1, but, forrandom dictionaries this condition is rarely satised. moreover, we give better estimationsthen the ones given recently by cai, wang and xu.