Polynomial-time data reduction for dominating set
Journal of the ACM (JACM)
Invitation to data reduction and problem kernelization
ACM SIGACT News
A Problem Kernelization for Graph Packing
SOFSEM '09 Proceedings of the 35th Conference on Current Trends in Theory and Practice of Computer Science
The parameterized complexity of the induced matching problem
Discrete Applied Mathematics
Linear Kernel for Planar Connected Dominating Set
TAMC '09 Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation
Incompressibility through Colors and IDs
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Kernelization: New Upper and Lower Bound Techniques
Parameterized and Exact Computation
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
An improved kernel for planar connected dominating set
TAMC'11 Proceedings of the 8th annual conference on Theory and applications of models of computation
New parameterized algorithms for the edge dominating set problem
MFCS'11 Proceedings of the 36th international conference on Mathematical foundations of computer science
Connectivity is not a limit for kernelization: planar connected dominating set
LATIN'10 Proceedings of the 9th Latin American conference on Theoretical Informatics
Linear problem kernels for NP-hard problems on planar graphs
ICALP'07 Proceedings of the 34th international conference on Automata, Languages and Programming
Note: Towards optimal kernel for connected vertex cover in planar graphs
Discrete Applied Mathematics
Towards optimal kernel for edge-disjoint triangle packing
Information Processing Letters
Hi-index | 0.00 |
A simple partition of the vertex set of a graph is introduced to analyze kernels for planar graph problems in which vertices and edges not in a solution have small distance to the solution. This method directly leads to improved kernel sizes for several problems, without needing new reduction rules. Moreover, new kernelization algorithms are developed for Connected Vertex Cover, Edge Dominating Set, and Maximum Triangle Packing problems, further improving the kernel sizes for these problems.