A new approach to the maximum-flow problem
Journal of the ACM (JACM)
A data structure for dynamic trees
Journal of Computer and System Sciences
Analysis of preflow push algorithms for maximum network flow
SIAM Journal on Computing
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
A faster deterministic maximum flow algorithm
SODA selected papers from the third annual ACM-SIAM symposium on Discrete algorithms
An efficient implementation of a scaling minimum-cost flow algorithm
Journal of Algorithms
Beyond the flow decomposition barrier
Journal of the ACM (JACM)
Experimental study of minimum cut algorithms
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
Journal of the ACM (JACM)
LEDA: a platform for combinatorial and geometric computing
LEDA: a platform for combinatorial and geometric computing
EFFICIENT GRAPH ALGORITHMS FOR SEQUENTIAL AND PARALLEL COMPUTERS
EFFICIENT GRAPH ALGORITHMS FOR SEQUENTIAL AND PARALLEL COMPUTERS
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Level Push-Relabel Algorithm for the Maximum Flow Problem
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
Solving maximum flow problems on real-world bipartite graphs
Journal of Experimental Algorithmics (JEA)
Communication: Simplifying maximum flow computations: The effect of shrinking and good initial flows
Discrete Applied Mathematics
A distributed mincut/maxflow algorithm combining path augmentation and push-relabel
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Contraction network for solving maximum flow problem
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
| Hi-index | 0.00 |
The maximum flow problem is a classical optimization problem with many applications. For a long time, HI-PR, an efficient implementation of the highest-label push-relabel algorithm, has been a benchmark due to its robust performance. We propose another variant of the push-relabel method, the partial augment-relabel (PAR) algorithm. Our experiments show that PAR is very robust. It outperforms HI-PR on all problem families tested, asymptotically in some cases.