Online Vertex-Weighted Bipartite Matching and Single-bid Budgeted Allocations

TL;DR

This paper presents an optimal randomized algorithm for online vertex-weighted bipartite matching, extending classic results to general weights and providing new insights into online allocation problems.

Contribution

It introduces the first optimal algorithm for vertex-weighted online bipartite matching using weight perturbations, generalizing previous unweighted algorithms.

Findings

Achieves a (1 - 1/e)-competitive ratio for weighted case

Extends online bipartite matching to general vertex weights

Provides new methods for online budgeted allocations

Abstract

We study the following vertex-weighted online bipartite matching problem: $G(U, V, E)$ is a bipartite graph. The vertices in $U$ have weights and are known ahead of time, while the vertices in $V$ arrive online in an arbitrary order and have to be matched upon arrival. The goal is to maximize the sum of weights of the matched vertices in $U$. When all the weights are equal, this reduces to the classic \emph{online bipartite matching} problem for which Karp, Vazirani and Vazirani gave an optimal $\left(1-\frac{1}{e}\right)$-competitive algorithm in their seminal work~\cite{KVV90}. Our main result is an optimal $\left(1-\frac{1}{e}\right)$-competitive randomized algorithm for general vertex weights. We use \emph{random perturbations} of weights by appropriately chosen multiplicative factors. Our solution constitutes the first known generalization of the algorithm in~\cite{KVV90} in this model and provides new insights into the role of randomization in online allocation problems. It also effectively solves the problem of \emph{online budgeted allocations} \cite{MSVV05} in the case when an agent makes the same bid for any desired item, even if the bid is comparable to his budget - complementing the results of \cite{MSVV05, BJN07} which apply when the bids are much smaller than the budgets.