Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Polynomial-time data reduction for dominating set
Journal of the ACM (JACM)
Parametric duality and kernelization: lower bounds and upper bounds on kernel size
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Invitation to data reduction and problem kernelization
ACM SIGACT News
Parameterized algorithmics for linear arrangement problems
Discrete Applied Mathematics
The parameterized complexity of the induced matching problem
Discrete Applied Mathematics
Kernels: annotated, proper and induced
IWPEC'06 Proceedings of the Second international conference on Parameterized and Exact Computation
Linear problem kernels for NP-hard problems on planar graphs
ICALP'07 Proceedings of the 34th international conference on Automata, Languages and Programming
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Data reduction by polynomial-time preprocessing is a core concept of (parameterized) complexity analysis in solving NP-hard problems. Its practical usefulness is confirmed by experimental work. Here, generalizing and extending previous work, we present a set of data reduction preprocessing rules on the way to compute optimal dominating sets in graphs. In this way, we arrive at the novel notion of “data reduction schemes.” In addition, we obtain data reduction results for domination in directed graphs that allow to prove a linear-size problem kernel for Directed Dominating Set in planar graphs.