Minimum-Cost Flow Algorithms on Randomized SourceāSink Networks
Experimental Study of Network Flow Optimization Algorithms - Graduate Course Project, Concordia University, Montreal, Quebec, [November, 01, 2024]
- Implemented the Successive Shortest Path algorithm from scratch, including residual graph construction, BellmanāFord based minimum-cost path extraction, and flow augmentation logic.
- Designed and executed large-scale experimental evaluation on randomized Euclidean directed graphs across 28 configurations with varying density (š), capacity bounds, and cost regimes.
- Compared SSP, Capacity Scaling, Scaling-SSP, and PrimalāDual algorithms using metrics including total cost, flow value, number of augmenting paths, mean path length, and proportional path length.
- Demonstrated that the PrimalāDual algorithm consistently achieves optimal minimum cost, while SSP achieves competitive performance in sparse regimes and degrades in dense graphs.
- Project Report Click here
- Codes and GitHub Repository: Click here