Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Efficient Approach Toward the Asymptotic Analysis of Node-Based
  Recovery Algorithms in Compressed Sensing   In this paper, we propose a general framework for the asymptotic analysis of
node-based verification-based algorithms. In our analysis we tend the signal
length $n$ to infinity. We also let the number of non-zero elements of the
signal $k$ scale linearly with $n$. Using the proposed framework, we study the
asymptotic behavior of the recovery algorithms over random sparse matrices
(graphs) in the context of compressive sensing. Our analysis shows that there
exists a success threshold on the density ratio $k/n$, before which the
recovery algorithms are successful, and beyond which they fail. This threshold
is a function of both the graph and the recovery algorithm. We also demonstrate
that there is a good agreement between the asymptotic behavior of recovery
algorithms and finite length simulations for moderately large values of $n$.

12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th