Computing Maximum Flow with Augmenting Electrical Flows

We present an Õ (m 7/10 U 1/7)-time algorithm for the maximum s-t flow problem (and the minimum s-t cut problem) in directed graphs with m arcs and largest integer capacity U. This matches the running time of the Õ (mU)10/7)- time algorithm of Madry [30] in the unit-capacity case, and improves over it, as well as over the Õ (m√n log U)-time algorithm of Lee and Sidford [25], whenever U is moderately large and the graph is sufficiently sparse. By well-known reductions, this also implies similar running time improvements for the maximum-cardinality bipartite b-matching problem. One of the advantages of our algorithm is that it is significantly simpler than the ones presented in [30] and [25]. In particular, these algorithms employ a sophisticated interior-point method framework, while our algorithm is cast directly in the classic augmenting path setting that almost all the combinatorial maximum flow algorithms use. At a high level, the presented algorithm takes a primal dual approach in which each iteration uses electrical flows computations both to find an augmenting s-t flow in the current residual graph and to update the dual solution. We show that by maintain certain careful coupling of these primal and dual solutions we are always guaranteed to make significant progress.

Authors

• Aleksander Madry

Keywords

• Algorithm design and analysis
• Couplings
• Context
• Approximation algorithms
• Heuristic algorithms
• Perturbation methods
• Computer science
• Maximum Flow
• Electric Flow
• Running Time
• Directed Graph
• Flow Problem
• Interior Point Method
• Matching Problem
• Flow Algorithm
• Dual Solution
• Minimum Cut
• Current Graph
• Step Size
• Preconditioning
• Linear System
• Normal Vector
• First Approximation
• Line Of Work
• Flow Values
• Flow Estimation
• Amount Of Flow
• Pair Of Solutions
• Residual Capacity
• Input Graph
• Ohm’s Law
• Coupling Conditions
• Original Graph
• Cut Set
• Amount Of Progress
• Sparse Graph
• Number Of Arcs
• maximum flow problem; augmenting paths; minimum