This post follows up on the Online Bipartite Matching series, specifically building on the primal-dual techniques discussed in Part II. Recently, I attended a talk by Yiding Feng at SJTU that introduced a fascinating application of these techniques in a broader context, as detailed in their paper.
Batching and Optimal Multi-stage Bipartite Allocations Yiding Feng and Rad Niazadeh
In several applications of real-time matching of demand to supply in online marketplaces, the platform allows for some latency to batch the demand and improve the efficiency of the resulting matching. Motivated by these applications, we study the optimal trade-off between batching and inefficiency in the context of designing robust online allocations. As our base model, we consider K-stage variants of the classic vertex weighted bipartite b-matching in the adversarial setting, where online vertices arrive stage-wise and in K batches — in contrast to online arrival. Our main result for this problem is an optimal 1−(1−1/K)^K- competitive (fractional) matching algorithm, improving the classic (1 − 1/e) competitive ratio bound known for its online variant (Mehta et al., 2007; Aggarwal et al., 2011). We also extend this result to the rich model of multi-stage configuration allocation with free-disposals (Devanur et al., 2016), which is motivated by the display advertising application in the context of video streaming platforms.
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