178,359 research outputs found
Improved Competitive Ratio for Edge-Weighted Online Stochastic Matching
We consider the edge-weighted online stochastic matching problem, in which an
edge-weighted bipartite graph G=(I\cup J, E) with offline vertices J and online
vertex types I is given. The online vertices have types sampled from I with
probability proportional to the arrival rates of online vertex types. The
online algorithm must make immediate and irrevocable matching decisions with
the objective of maximizing the total weight of the matching. For the problem
with general arrival rates, Feldman et al. (FOCS 2009) proposed the Suggested
Matching algorithm and showed that it achieves a competitive ratio of 1-1/e
\approx 0.632. The ratio has recently been improved to 0.645 by Yan (2022), who
proposed the Multistage Suggested Matching (MSM) algorithm. In this paper, we
propose the Evolving Suggested Matching (ESM) algorithm, and show that it
achieves a competitive ratio of 0.650.Comment: To appear in WINE202
On the Perturbation Function of Ranking and Balance for Weighted Online Bipartite Matching
Ranking and Balance are arguably the two most important algorithms in the online matching literature. They achieve the same optimal competitive ratio of 1-1/e for the integral version and fractional version of online bipartite matching by Karp, Vazirani, and Vazirani (STOC 1990) respectively. The two algorithms have been generalized to weighted online bipartite matching problems, including vertex-weighted online bipartite matching and AdWords, by utilizing a perturbation function. The canonical choice of the perturbation function is f(x) = 1-e^{x-1} as it leads to the optimal competitive ratio of 1-1/e in both settings.
We advance the understanding of the weighted generalizations of Ranking and Balance in this paper, with a focus on studying the effect of different perturbation functions. First, we prove that the canonical perturbation function is the unique optimal perturbation function for vertex-weighted online bipartite matching. In stark contrast, all perturbation functions achieve the optimal competitive ratio of 1-1/e in the unweighted setting. Second, we prove that the generalization of Ranking to AdWords with unknown budgets using the canonical perturbation function is at most 0.624 competitive, refuting a conjecture of Vazirani (2021). More generally, as an application of the first result, we prove that no perturbation function leads to the prominent competitive ratio of 1-1/e by establishing an upper bound of 1-1/e-0.0003. Finally, we propose the online budget-additive welfare maximization problem that is intermediate between AdWords and AdWords with unknown budgets, and we design an optimal 1-1/e competitive algorithm by generalizing Balance
Edge-weighted Online Stochastic Matching: Beating
We study the edge-weighted online stochastic matching problem. Since Feldman,
Mehta, Mirrokni, and Muthukrishnan proposed the -competitive
Suggested Matching algorithm, there has been no improvement for the general
edge-weighted online stochastic matching problem. In this paper, we introduce
the first algorithm beating the barrier in this setting, achieving
a competitive ratio of . Under the LP proposed by Jaillet and Lu, we
design an algorithmic preprocessing, dividing all edges into two classes. Then
based on the Suggested Matching algorithm, we adjust the matching strategy to
improve the performance on one class in the early stage and on another class in
the late stage, while keeping the matching events of different edges highly
independent. By balancing them, we finally guarantee the matched probability of
every single edge
Online Matching with Stochastic Rewards: Optimal Competitive Ratio via Path Based Formulation
The problem of online matching with stochastic rewards is a generalization of
the online bipartite matching problem where each edge has a probability of
success. When a match is made it succeeds with the probability of the
corresponding edge. Introducing this model, Mehta and Panigrahi (FOCS 2012)
focused on the special case of identical edge probabilities. Comparing against
a deterministic offline LP, they showed that the Ranking algorithm of Karp et
al. (STOC 1990) is 0.534 competitive and proposed a new online algorithm with
an improved guarantee of for vanishingly small probabilities. For the
case of vanishingly small but heterogeneous probabilities Mehta et al. (SODA
2015), gave a 0.534 competitive algorithm against the same LP benchmark. For
the more general vertex-weighted version of the problem, to the best of our
knowledge, no results being were previously known even for identical
probabilities.
We focus on the vertex-weighted version and give two improvements. First, we
show that a natural generalization of the Perturbed-Greedy algorithm of
Aggarwal et al. (SODA 2011), is competitive when probabilities
decompose as a product of two factors, one corresponding to each vertex of the
edge. This is the best achievable guarantee as it includes the case of
identical probabilities and in particular, the classical online bipartite
matching problem. Second, we give a deterministic competitive algorithm
for the previously well studied case of fully heterogeneous but vanishingly
small edge probabilities. A key contribution of our approach is the use of
novel path-based analysis. This allows us to compare against the natural
benchmarks of adaptive offline algorithms that know the sequence of arrivals
and the edge probabilities in advance, but not the outcomes of potential
matches.Comment: Preliminary version in EC 202
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