Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse linear regression with compressed and low-precision data via
  concave quadratic programming   We consider the problem of the recovery of a k-sparse vector from compressed
linear measurements when data are corrupted by a quantization noise. When the
number of measurements is not sufficiently large, different $k$-sparse
solutions may be present in the feasible set, and the classical l1 approach may
be unsuccessful. For this motivation, we propose a non-convex quadratic
programming method, which exploits prior information on the magnitude of the
non-zero parameters. This results in a more efficient support recovery. We
provide sufficient conditions for successful recovery and numerical simulations
to illustrate the practical feasibility of the proposed method.

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The categories of this paper are: q-bio
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: q-alg