Approximate Turing Kernelization for Problems Parameterized by Treewidth

@inproceedings{Hols2020ApproximateTK,
  title={Approximate Turing Kernelization for Problems Parameterized by Treewidth},
  author={Eva-Maria C. Hols and Stefan Kratsch and A. Pieterse},
  booktitle={ESA},
  year={2020}
}
We extend the notion of lossy kernelization, introduced by Lokshtanov et al. [STOC 2017], to approximate Turing kernelization. An $\alpha$-approximate Turing kernel for a parameterized optimization problem is a polynomial-time algorithm that, when given access to an oracle that outputs $c$-approximate solutions in $O(1)$ time, obtains an $(\alpha \cdot c)$-approximate solution to the considered problem, using calls to the oracle of size at most $f(k)$ for some function $f$ that only depends on… Expand
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References

SHOWING 1-10 OF 50 REFERENCES
A Hierarchy of Polynomial Kernels
(Meta) Kernelization
Infeasibility of instance compression and succinct PCPs for NP
On problems without polynomial kernels
Representative Sets and Irrelevant Vertices: New Tools for Kernelization
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