Online and Approximation Algorithms for Clustering and Network Design
Solving nonconvex formulations of Euclidean steiner tree problems in N-space
Bayesian additive regression trees for regression discontinuity designs
| Full text | |
| Author(s): |
Chaves Pedrosa, Lehilton Lelis
;
Kasuya Rosado, Hugo Kooki
Total Authors: 2
|
| Document type: | Journal article |
| Source: | ALGORITHMICA; v. N/A, p. 37-pg., 2022-01-19. |
| Abstract | |
We consider the k-PRIZE- COLLECTING STEINER TREE PROBLEM. An instance is composed of an integer k and a graph G with costs on edges and penalties on vertices. The objective is to find a tree spanning at least k vertices which minimizes the cost of the edges in the tree plus the penalties of vertices not in the tree. This is one of the most fundamental network design problems and is a common generalization of the PRIZE- COLLECTING STEINER TREE PROBLEM and the k-MINIMUM SPANNING TREE PROBLEM. Our main result is a 2-approximation algorithm, which improves on the currently best known approximation factor of 3.96 and has a faster running time. The algorithm builds on a modification of the primal-dual framework of Goemans and Williamson, and reveals interesting properties that can be applied to other similar problems. (AU) | |
| FAPESP's process: | 15/11937-9 - Investigation of hard problems from the algorithmic and structural stand points |
| Grantee: | Flávio Keidi Miyazawa |
| Support Opportunities: | Research Projects - Thematic Grants |