Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal-size problem kernels for $d$-Hitting Set in linear time and
  space   The known linear-time kernelizations for $d$-Hitting Set guarantee linear
worst-case running times using a quadratic-size data structure (that is not
fully initialized). Getting rid of this data structure, we show that problem
kernels of asymptotically optimal size $O(k^d)$ for $d$-Hitting Set are
computable in linear time and space. Additionally, we experimentally compare
the linear-time kernelizations for $d$-Hitting Set to each other and to a
classical data reduction algorithm due to Weihe.

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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
3
The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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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: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: eess