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Distributed stochastic optimization via correlated scheduling

This talk has two parts. The first part considers a problem where multiple devices make repeated decisions based on their own observed events. The events and decisions at each time step determine the values of a utility function and a collection of penalty functions. The goal is to make distributed decisions over time to maximize time average utility subject to time average constraints on the penalties. An example is a collection of power constrained sensors that repeatedly report their own observations to a fusion center. Maximum time average utility is fundamentally reduced because devices do not know the events observed by others. Optimality is characterized for this distributed context. It is shown that optimality is achieved by correlating device decisions through a commonly known pseudorandom sequence. An optimal algorithm is developed that chooses pure strategies at each time step based on a set of time-varying weights.

The second part considers a related stochastic game structure, but allows players to share partial information and baseline decisions with a centralized game manager. An algorithm is developed for making management suggestions that, if taken, lead to no-regret policies. Results in this game setting apply to arbitrary sample paths of random events and arbitrary baseline decisions made by human players.

These results are based on two papers, found at the following links:
Journal version
arXiv version

Michael J. Neely received B.S. degrees in both Electrical Engineering and Mathematics from the University of Maryland, College Park, in 1997. He was then awarded a 3 year Department of Defense NDSEG Fellowship for graduate study at the Massachusetts Institute of Technology, where he received an M.S. degree in 1999 and a Ph.D. in 2003, both in Electrical Engineering. He joined the faculty of Electrical Engineering at the University of Southern California in 2004, where he is currently an Associate Professor. His research interests are in the areas of stochastic network optimization and queueing theory, with applications to wireless networks, mobile ad-hoc networks, and switching systems. Michael received the NSF Career award in 2008 and the Viterbi School of Engineering Junior Research Award in 2009. He is a member of Tau Beta Pi and Phi Beta Kappa.

distributed_stochastic_optimization_via_correlated_scheduling.txt · Last modified: 2016/09/01 19:15 (external edit)