On the Assadi Liu Tarjan Auction Algorithm for Bipartite Matching: Simplification, Alternative Analysis, and Hard Instance

2026-07-08

AI Summary

This work addresses longstanding challenges in (1−ε)-approximation algorithms for maximum matching in bipartite graphs—namely, algorithmic complexity, insufficient theoretical understanding, and the absence of tight lower-bound instances. Revisiting the ALT auction algorithm, we eliminate its original vertex-freezing mechanism and introduce a novel analysis framework grounded in augmenting paths. This new perspective not only simplifies the algorithm’s structure but also provides an intuitive explanation of its convergence behavior. Moreover, we construct the first hard instance requiring Ω(1/ε²) rounds of iteration, demonstrating that this round complexity is tight even on simple path graphs and thereby establishing the fundamental theoretical limit of the algorithm.

Abstract

Assadi, Liu, and Tarjan [SOSA'21] gave an auction algorithm that outputs a $(1-ε)$-approximation to Maximum Matching in bipartite graphs. Their algorithm computes a sequence of $O(\frac{1}{ε^2})$ maximal matchings in subgraphs of the input graph and can be implemented in the multi-pass streaming setting with $O(\frac{1}{ε^2})$ passes in a straightforward manner, which constitutes the state-of-the-art pass/approximation trade-off result in the multi-pass streaming setting. Their analysis uses tools from combinatorial auctions and, at its heart, relies on a clever potential function argument. Their proof, however, provides only limited insight into the inner workings of the algorithm. In this paper, we revisit the ALT-algorithm and present the following contributions. Simplification: The ALT-algorithm is built upon a freezing mechanism where vertices on one side of the bipartition that have already been rematched $Θ(\frac{1}ε)$ times over the course of the algorithm remain matched to their current partner forever. We show that this mechanism is in fact unnecessary, i.e., no special treatment of such vertices is needed. Alternative Analysis: We give an alternative analysis of the algorithm that is based on augmenting paths. Our analysis allows for a reinterpretation as one that follows the traditional approach of searching for and eliminating augmenting paths. Our analysis also copes with the removal of the freezing mechanism in a natural way, whereas the analysis of Assadi et al. strictly depends on its use. Hard Instance: We provide the first hard instance on which the algorithm requires $Ω(\frac{1}{ε^2})$ iterations/maximal matching computations. The instance is a simple path graph, where we exhibit a cyclic behaviour that prevents fast progress.

Problem

• bipartite matching
• auction algorithm
• approximation algorithm
• streaming algorithm
• hard instance

Innovation

• auction algorithm
• bipartite matching
• augmenting paths
• streaming algorithms
• hard instance