A Multivariate Framework for Weighted FPT Algorithms
Hadas Shachnai, Meirav Zehavi

TL;DR
This paper presents a multivariate framework that significantly improves the efficiency of algorithms for weighted graph problems by leveraging the minimum solution size, achieving near-unweighted running times.
Contribution
The authors introduce a novel multivariate approach that refines weighted parameterized algorithms, reducing their running times to depend on the minimal solution size rather than the weight parameter.
Findings
Improved algorithms for weighted Vertex Cover, 3-Hitting Set, Edge Dominating Set, and Max Internal Out-Branching.
Running times depend on the minimal solution size s, often much smaller than W.
Algorithms match the efficiency of unweighted variants for weighted problems.
Abstract
We introduce a novel multivariate approach for solving weighted parameterized problems. In our model, given an instance of size of a minimization (maximization) problem, and a parameter , we seek a solution of weight at most (or at least) . We use our general framework to obtain efficient algorithms for such fundamental graph problems as Vertex Cover, 3-Hitting Set, Edge Dominating Set and Max Internal Out-Branching. The best known algorithms for these problems admit running times of the form , for some constant . We improve these running times to , where is the minimum size of a solution of weight at most (at least) . If no such solution exists, , where is the maximum size of a solution. Clearly, can be substantially smaller than . In particular, the running times of our algorithms are (almost) the…
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