Approximation algorithms for geometric median problems
Information Processing Letters
Approximation algorithms for facility location problems (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Analysis of a local search heuristic for facility location problems
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Greedy strikes back: improved facility location algorithms
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Approximation algorithms for facility location problems
APPROX '00 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization
A Greedy Facility Location Algorithm Analyzed Using Dual Fitting
APPROX '01/RANDOM '01 Proceedings of the 4th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems and 5th International Workshop on Randomization and Approximation Techniques in Computer Science: Approximation, Randomization and Combinatorial Optimization
Improved Combinatorial Algorithms for the Facility Location and k-Median Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Primal-Dual Approximation Algorithms for Metric Facility Location and k-Median Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
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This paper presents several methods to significantly speed up the local search algorithm for the facility location clustering. Most papers regarding the facility location focus on the mathematical theory, tight approximation ratios, and solid proofs. While this is certainly important, the papers rarely concern the practical application of the proposed algorithms. A straightforward, naive approach is often not efficient, so special techniques must be used to achieve high performance. This paper focuses on an acceleration of the local search algorithm. Specially, it proposes an acceleration using a space partitioning and a parallelisation on a desktop computer.