A data structure for dynamic trees
Journal of Computer and System Sciences
Scheduling project networks with resource constraints and time windows
Annals of Operations Research
Scheduling with forbidden sets
Discrete Applied Mathematics - Special issue on models and algorithms for planning and scheduling problems
Sparsification—a technique for speeding up dynamic graph algorithms
Journal of the ACM (JACM)
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Efficiency of a Good But Not Linear Set Union Algorithm
Journal of the ACM (JACM)
A minimum spanning tree algorithm with inverse-Ackermann type complexity
Journal of the ACM (JACM)
Introduction to Algorithms
Maintaining Minimum Spanning Trees in Dynamic Graphs
ICALP '97 Proceedings of the 24th International Colloquium on Automata, Languages and Programming
On the hardness of reoptimization
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
Reoptimization of the maximum weighted Pk-free subgraph problem under vertex insertion
WALCOM'12 Proceedings of the 6th international conference on Algorithms and computation
Reoptimization of some maximum weight induced hereditary subgraph problems
LATIN'12 Proceedings of the 10th Latin American international conference on Theoretical Informatics
Reoptimizing the rural postman problem
Computers and Operations Research
Reoptimization of maximum weight induced hereditary subgraph problems
Theoretical Computer Science
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We study reoptimization versions of the minimum spanning tree problem. The reoptimization setting can generally be formulated as follows: given an instance of the problem for which we already know some optimal solution, and given some ''small'' perturbations on this instance, is it possible to compute a new (optimal or at least near-optimal) solution for the modified instance without ex nihilo computation? We focus on two kinds of modifications: node-insertions and node-deletions. When k new nodes are inserted together with their incident edges, we mainly propose a fast strategy with complexity O(kn) which provides a max{2,3-(2/(k-1))}-approximation ratio, in complete metric graphs and another one that is optimal with complexity O(nlogn). On the other hand, when k nodes are deleted, we devise a strategy which in O(n) achieves approximation ratio bounded above by 2@?|L"m"a"x|/2@? in complete metric graphs, where L"m"a"x is the longest deleted path and |L"m"a"x| is the number of its edges. For any of the approximation strategies, we also provide lower bounds on their approximation ratios.