Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tight Sufficient Conditions on Exact Sparsity Pattern Recovery   A noisy underdetermined system of linear equations is considered in which a
sparse vector (a vector with a few nonzero elements) is subject to measurement.
The measurement matrix elements are drawn from a Gaussian distribution. We
study the information-theoretic constraints on exact support recovery of a
sparse vector from the measurement vector and matrix. We compute a tight,
sufficient condition that is applied to ergodic wide-sense stationary sparse
vectors. We compare our results with the existing bounds and recovery
conditions. Finally, we extend our results to approximately sparse signals.

16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph