Algorithms for Placing Monitors in a Flow Network
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
The Parameterized Complexity of k-Flip Local Search for SAT and MAX SAT
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Local search: is brute-force avoidable?
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Local search: is brute-force avoidable?
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A moderately exponential time algorithm for full degree spanning tree
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Sharp separation and applications to exact and parameterized algorithms
LATIN'10 Proceedings of the 9th Latin American conference on Theoretical Informatics
Local search: Is brute-force avoidable?
Journal of Computer and System Sciences
Fixed-parameter tractability results for full-degree spanning tree and its dual
IWPEC'06 Proceedings of the Second international conference on Parameterized and Exact Computation
A survey of approximation results for local search algorithms
Efficient Approximation and Online Algorithms
On the directed Full Degree Spanning Tree problem
Discrete Optimization
The parameterized complexity of k-flip local search for SAT and MAX SAT
Discrete Optimization
Parameterized complexity results for exact bayesian network structure learning
Journal of Artificial Intelligence Research
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This work is motivated by the problem of placing pressure-meters in fluid networks. The problem is formally defined in graph-theoretic terms as follows. Given a graph, find a cotree (complement of a tree) incident upon the minimum number of vertices. We show that this problem is NP-hard and MAX SNP-hard. We design an algorithm with an approximation factor of $2 + \epsilon$ for this problem for any fixed $\epsilon 0$. This approximation bound comes from the analysis of a local search heuristic, a common practical optimization technique that does not often allow formal worst-case analysis. The algorithm is made very efficient by finding restrictive definitions of the local neighborhoods to be searched. We also exhibit a polynomial time approximation scheme for this problem when the input is restricted to planar graphs.