An Online Algorithm for Maximizing Submodular Functions

We present an algorithm for solving a broad class of online resource allocation . Our online algorithm can be applied in environments where abstract jobs arrive one at a time, and one can complete the jobs by investing time in a f abstract activities, according to some schedule. We assume that the fraction of jobs completed by a schedule is a monotone, submodular function of a set of pairs (v, ), where is the time invested in activity v. Under this assumption, our online algorithm performs near-optimally according to two natural metrics: (i) the fraction of jobs completed within time T, for some fixed deadline T > 0, and (ii) the average time required to complete each job. We evaluate our algorithm experimentally by using it to learn, online, a schedule for allocating CPU time among solvers entered in the 2007 SAT solver competition.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Matthew J. Streeter, Daniel Golovin
• Keywords: Algorithm, Information Technology, NIPS 2008, Online Algorithm, Online Resource Allocation