Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Demixing Structured Superposition Signals from Periodic and Aperiodic
  Nonlinear Observations   We consider the demixing problem of two (or more) structured high-dimensional
vectors from a limited number of nonlinear observations where this nonlinearity
is due to either a periodic or an aperiodic function. We study certain families
of structured superposition models, and propose a method which provably
recovers the components given (nearly) $m = \mathcal{O}(s)$ samples where $s$
denotes the sparsity level of the underlying components. This strictly improves
upon previous nonlinear demixing techniques and asymptotically matches the best
possible sample complexity. We also provide a range of simulations to
illustrate the performance of the proposed algorithms.

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The categories of this paper are: cs
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: math-ph
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The categories of this paper are: math
35
The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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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: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: physics