Scheduling with forbidden sets
Discrete Applied Mathematics - Special issue on models and algorithms for planning and scheduling problems
Algorithmics for hard problems: introduction to combinatorial optimization, randomization, approximation, and heuristics
Reoptimization of Steiner Trees
SWAT '08 Proceedings of the 11th Scandinavian workshop on Algorithm Theory
Reoptimization of the Metric Deadline TSP
MFCS '08 Proceedings of the 33rd international symposium on Mathematical Foundations of Computer Science
Reoptimization of Traveling Salesperson Problems: Changing Single Edge-Weights
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
Reoptimization of Steiner trees: Changing the terminal set
Theoretical Computer Science
On the hardness of reoptimization
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
Knowing all optimal solutions does not help for TSP reoptimization
Computation, cooperation, and life
Reoptimization of minimum and maximum traveling salesman's tours
SWAT'06 Proceedings of the 10th Scandinavian conference on Algorithm Theory
Steiner tree reoptimization in graphs with sharpened triangle inequality
Journal of Discrete Algorithms
Reoptimization of maximum weight induced hereditary subgraph problems
Theoretical Computer Science
| Hi-index | 0.00 |
In reoptimization, we consider the following scenario: Given an instance of a hard optimization problem together with an optimal solution for it, we want to solve a locally modified instance of the problem. It has recently been shown for several hard optimization problems that their corresponding reoptimization variants remain 𝒩𝒫-hard or even hard to approximate whereas they often admit improved approximation ratios. In this paper, we investigate a generalization of the reoptimization concept where we are given not only one optimal solution but multiple optimal solutions for an instance. We prove, for some variants of the Steiner tree problem and the traveling salesman problem, that the known reoptimization hardness results carry over to this generalized setting. Moreover, we consider the performance of local search strategies on reoptimization problems. We show that local search does not work for solving TSP reoptimization, even in the presence of multiple solutions.