# 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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