Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/309813 
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] Computational Optimization and Applications [ISSN:] 1573-2894 [Volume:] 85 [Issue:] 1 [Publisher:] Springer US [Place:] New York, NY [Year:] 2023 [Pages:] 213-246
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
Computing Wasserstein barycenters of discrete measures has recently attracted considerable attention due to its wide variety of applications in data science. In general, this problem is NP-hard, calling for practical approximative algorithms. In this paper, we analyze a well-known simple framework for approximating Wasserstein- pbarycenters, where we mainly consider the most common case p=2and p=1, which is not as well discussed. The framework produces sparse support solutions and shows good numerical results in the free-support setting. Depending on the desired level of accuracy, this requires only N-1or N(N-1)/2standard two-marginal optimal transport (OT) computations between the Ninput measures, respectively, which is fast, memory-efficient and easy to implement using any OT solver as a black box. What is more, these methods yield a relative error of at most Nand 2, respectively, for both p=1,2. We show that these bounds are practically sharp. In light of the hardness of the problem, it is not surprising that such guarantees cannot be close to optimality in general. Nevertheless, these error bounds usually turn out to be drastically lower for a given particular problem in practice and can be evaluated with almost no computational overhead, in particular without knowledge of the optimal solution. In our numerical experiments, this guaranteed errors of at most a few percent.
Schlagwörter: 
Optimal transport
Discrete Wasserstein barycenter
Approximative algorithm
Error analysis
Sparsity
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