We study a dynamic non-bipartite matching problem. There is a fixed set of agent types, and agents of a given type arrive and depart according to type-specific Poisson processes.  The value of a match is determined by the types of the matched agents. We present an online algorithm that is (1/6)-competitive with respect to the value of the optimal-in-hindsight policy, for arbitrary weighted graphs . This is the first result to achieve a constant competitive ratio when both arrivals and departures are random and unannounced . Our algorithm treats agents heterogeneously, interpolating between immediate and delayed matching in order to thicken the market while still matching valuable agents opportunistically.
We study a dynamic non-bipartite matching problem. There is a fixed set of agent types, and agents of a given type arrive and depart according to type-specific Poisson processes. Agent departures are not announced in advance. The value of a match is determined by the types of the matched agents. We present an online algorithm that is (1/6)-competitive with respect to the value of the optimal-in-hindsight policy, for arbitrary weighted graphs . Our algorithm treats agents heterogeneously, interpolating between immediate and delayed matching in order to thicken the market while still matching valuable agents opportunistically.
A
Agent departures are not announced in advance.
D
. This is the first result to achieve a constant competitive ratio when both arrivals and departures are random and unannounced