Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience

Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan

We study several generalizations of the Online Bipartite Matching problem. We consider settings with stochastic rewards, patience constraints, and weights (both vertex- and edge-weighted variants). We introduce a stochastic variant of the patience-constrained problem, where the patience is chosen randomly according to some known distribution and is not known until the point at which patience has been exhausted. We also consider stochastic arrival settings (i.e., online vertex arrival is determined by a known random process), which are natural settings that are able to beat the hard worst-case bounds of more pessimistic adversarial arrivals. Our approach to online matching utilizes black-box algorithms for matching on star graphs under various models of patience. In support of this, we design algorithms which solve the star graph problem optimally for geometrically-distributed patience and yield a 1/2-approximation for any patience distribution. This 1/2-approximation also improves existing guarantees for cascade-click models in the product ranking literature, in which a user must be shown a sequence of items with various click-through-rates and the user's patience could run out at any time. We then build a framework which uses these star graph algorithms as black boxes to solve the online matching problems under different arrival settings. We show improved (or first-known) competitive ratios for these problems. We also present negative results that include formalizing the concept of a stochasticity gap for LP upper bounds on these problems, showing some new stochasticity gaps for popular LPs, and bounding the worst-case performance of some greedy approaches.

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