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(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. •Linear-time and space kernelizations for d-Hitting Set.•Experimental comparison to well-known data reduction algorithm of Weihe.•For low parameter values, kernelization is superior.