Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse Approximation by Semidefinite Programming   The problem of sparse approximation and the closely related compressed
sensing have received tremendous attention in the past decade. Primarily
studied from the viewpoint of applied harmonic analysis and signal processing,
there have been two dominant algorithmic approaches to this problem: Greedy
methods called the matching pursuit (MP) and the linear programming based
approaches called the basis pursuit (BP). The aim of the current paper is to
bring a fresh perspective to sparse approximation by treating it as a
combinatorial optimization problem and providing an algorithm based on the
powerful optimization technique semidefinite programming (SDP). In particular,
we show that there is a randomized algorithm based on a semidefinite relaxation
of the problem with performance guarantees depending on the coherence and the
restricted isometry constant of the dictionary used. We then show a
derandomization of the algorithm based on the method of conditional
probabilities.

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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph