Abstract
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(kd) 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.
Original language | English |
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Article number | 105998 |
Number of pages | 9 |
Journal | Information Processing Letters |
Volume | 163 |
DOIs | |
Publication status | Published - 1 Nov 2020 |
Keywords
- Combinatorial problems
- Data reduction
- Kernelization
- NP-hard problem
- Parameterized complexity