Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Theoretical Linear Convergence of Deep Unfolding Network for
  Block-Sparse Signal Recovery   In this paper, we consider the recovery of the high-dimensional block-sparse
signal from a compressed set of measurements, where the non-zero coefficients
of the recovered signal occur in a small number of blocks. Adopting the idea of
deep unfolding, we explore the block-sparse structure and put forward a
block-sparse reconstruction network named Ada-BlockLISTA, which performs
gradient descent on every single block followed by a block-wise shrinkage.
Furthermore, we prove the linear convergence rate of our proposed network,
which also theoretically guarantees exact recovery for a potentially higher
sparsity level based on underlyingblock structure. Numerical results indicate
that Ada-BlockLISTA yields better signal recovery performance compared with
existing algorithms, which ignore the additional block structure in the signal
model.

3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: stat