Optimal-size problem kernels for d-Hitting Set in linear time and space
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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 Weihe and evaluate their combinations.
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