—In this work we focus on a stochastic optimization based approach to make distributed routing and server management decisions in the context of large-scale, geographically distr...
Stochastic optimization arising from precoding in a multi-antenna fading channel with channel mean feedback to maximize data rates is important but challenging. The use of relayin...
Real-world networks often need to be designed under uncertainty, with only partial information and predictions of demand available at the outset of the design process. The field ...
Stochastic dominance relations are well-studied in statistics, decision theory and economics. Recently, there has been significant interest in introducing dominance relations into...
Deterministic optimization approaches have been well developed and widely used in the process industry to accomplish off-line and on-line process optimization. The challenging tas...
Abstract. Stochastic optimization is a leading approach to model optimization problems in which there is uncertainty in the input data, whether from measurement noise or an inabili...
The field of stochastic optimization studies decision making under uncertainty, when only probabilistic information about the future is available. Finding approximate solutions to...
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most...
This paper restates the shape from shading problem regarding both surface modeling and optimization. We combine the use of a B-spline as 3D model for the scene surface and the use...