Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved Recovery of Analysis Sparse Vectors in Presence of Prior
  Information   In this work, we consider the problem of recovering analysis-sparse signals
from under-sampled measurements when some prior information about the support
is available. We incorporate such information in the recovery stage by suitably
tuning the weights in a weighted $\ell_1$ analysis optimization problem.
Indeed, we try to set the weights such that the method succeeds with minimum
number of measurements. For this purpose, we exploit the upper-bound on the
statistical dimension of a certain cone to determine the weights. Our numerical
simulations confirm that the introduced method with tuned weights outperforms
the standard $\ell_1$ analysis technique.

8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas