We improve two linear-time data reduction algorithms for the d-Hitting Set problem to work in linear space, thus obtaining the first algorithms for computing problem kernels of asymptotically optimal size O(k^d) for d-Hitting Set  in linear time and space. We experimentally compare the two algorithms to a classical data reduction algorithm of Weiheand evaluate their combinations .
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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We improve two
The known
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data reduction algorithms for the
kernelizations for
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problem to work in linear space, thus obtaining the first algorithms for computing
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
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are computable
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We
Additionally, we
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two algorithms to
linear-time kernelizations for d-Hitting Set to each other and to
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of Weiheand evaluate their combinations
due to Weihe