Cut-Toggling and Cycle-Toggling for Electrical Flow and Other p-Norm Flows

TL;DR

This paper introduces a simplified, combinatorial approach using cycle and cut toggling to efficiently compute flows minimizing the p-norm in undirected graphs, unifying solutions for various p values.

Contribution

It presents a novel, efficient implementation for p=2 and near-optimal algorithms for all p > 1, revealing new connections to dynamic graph data structures.

Findings

Efficient algorithms for p=2 flows using cycle and cut toggling.

Near-optimal p-norm flow solutions for all p > 1 with fewer iterations.

Simpler, more combinatorial algorithms compared to existing methods.

Abstract

We study the problem of finding flows in undirected graphs so as to minimize the weighted $p$-norm of the flow for any $p > 1$. When $p=2$, the problem is that of finding an electrical flow, and its dual is equivalent to solving a Laplacian linear system. The case $p = \infty$ corresponds to finding a min-congestion flow, which is equivalent to max-flows. A typical algorithmic construction for such problems considers vertex potentials corresponding to the flow conservation constraints, and has two simple types of update steps: cycle toggling, which modifies the flow along a cycle, and cut toggling, which modifies all potentials on one side of a cut. Both types of steps are typically performed relative to a spanning tree $T$; then the cycle is a fundamental cycle of $T$, and the cut is a fundamental cut of $T$. In this paper, we show that these simple steps can be used to give a novel efficient implementation for the $p = 2$ case and to find near-optimal $p$-norm flows in a low number of iterations for all values of $p > 1$. Compared to known faster algorithms for these problems, our algorithms are simpler, more combinatorial, and also expose several underlying connections between these algorithms and dynamic graph data structures that have not been formalized previously.