 i.e., p_{u,v = p_v for all (u,v) \in E. }%DIFDELCMD < \\ %%% (3) When the patience constraint \ell_v satisfies \ell_v \in \{ 1,|U|\} for every online vertex; i.e., every online vertex either has unit or full patience. Setting the probability p_e = 1 for all e \in E, the stochastic problem becomes the classical online bipartite matching problem .
<meaning-changed>  i.e., p_{u,v = p_v for all (u,v) \in E. }%DIFDELCMD < \\ %%% (3) When the patience constraint \ell_v satisfies \ell_v \in \{ 1,|U|\} for every online vertex; i.e., every online vertex either has unit or full patience. Setting the probability p_e = 1 for all e \in E, the stochastic problem becomes the classical online bipartite matching problem .
 i.e., p_{u,v = p_v for all (u,v) \in E. }%DIFDELCMD < \\ %%% (3) When the patience constraint \ell_v satisfies \ell_v \in \{ This model generalizes the various settings of the classical bipartite matching problem .