Efficient Online Sensitivity Analysis For The Injective Bottleneck Path Problem

TL;DR

This paper introduces an efficient online sensitivity analysis method for the injective bottleneck path problem, significantly improving the computational complexity for determining edge tolerances in network paths with multiple source-target pairs.

Contribution

It proposes a novel preprocessing algorithm that computes all edge tolerances for multiple source-target pairs in a network more efficiently than existing methods.

Findings

Reduces complexity of tolerance computation to O(m α(m,n) + min((n+k) log n, km))

Achieves faster tolerance analysis for multiple pairs compared to previous algorithms

Provides an asymptotic improvement over the Ramaswamy-Orlin-Chakravarty's algorithm

Abstract

The tolerance of an element of a combinatorial optimization problem with respect to a given optimal solution is the maximum change, i.e., decrease or increase, of its cost, such that this solution remains optimal. The bottleneck path problem, for given an edge-capacitated graph, a source, and a target, is to find the $\max$-$\min$ value of edge capacities on paths between the source and the target. For any given sample of this problem with $n$ vertices and $m$ edges, there is known the Ramaswamy-Orlin-Chakravarty's algorithm to compute an optimal path and all tolerances with respect to it in $O(m+n\log n)$ time. In this paper, for any in advance given $(n,m)$-network with distinct edge capacities and $k$ source-target pairs, we propose an $O\Big(m \alpha(m,n)+\min\big((n+k)\log n,km\big)\Big)$-time preprocessing, where $\alpha(\cdot,\cdot)$ is the inverse Ackermann function, to find in $O(k)$ time all $2k$ tolerances of an arbitrary edge with respect to some $\max\min$ paths between the paired sources and targets. To find both tolerances of all edges with respect to those optimal paths, it asymptotically improves, for some $n,m,k$, the Ramaswamy-Orlin-Chakravarty's complexity $O\big(k(m+n\log n)\big)$ up to $O(m\alpha(n,m)+km)$.