Polynomial-Time Data Reduction for Weighted Problems Beyond Additive Goal Functions

1 Oct 2019 Matthias Bentert René van Bevern Till Fluschnik André Nichterlein Rolf Niedermeier

Dealing with NP-hard problems, kernelization is a fundamental notion for polynomial-time data reduction with performance guarantees: in polynomial time, a problem instance is reduced to an equivalent instance with size upper-bounded by a function of a parameter chosen in advance. Kernelization for weighted problems particularly requires to also shrink weights... (read more)

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  • DATA STRUCTURES AND ALGORITHMS
  • DISCRETE MATHEMATICS
  • OPTIMIZATION AND CONTROL
  • 90C27