Optimal online assignment with forecasts

Motivated by the allocation problem facing publishers in display advertising we formulate the online assignment with forecast problem, a version of the online allocation problem where the algorithm has access to random samples from the future set of arriving vertices. We provide a solution that allows us to serve Internet users in an online manner that is provably nearly optimal. Our technique applies to the forecast version of a large class of online assignment problems, such as online bipartite matching, allocation, and budgeted bidders, in which we wish to minimize the value of some convex objective function subject to a set of linear supply and demand constraints. Our solution utilizes a particular subspace of the dual space, allowing us to describe the optimal primal solution implicitly in space proportional to the demand side of the input graph. More importantly, it allows us to prove that representing the primal solution using such a compact allocation plan yields a robust onli...

• Type: Conference
• Year: 2010
• Where: SIGECOM
• Authors: Erik Vee, Sergei Vassilvitskii, Jayavel Shanmugasundaram
• Keywords: Allocation Problem, Compact Allocation Plan, ECommerce, Primal Solution, SIGECOM 2010