In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and...
Anton J. Kleywegt, Alexander Shapiro, Tito Homem-d...
Motivated by online ad allocation, we study the problem of simultaneous approximations for the adversarial and stochastic online budgeted allocation problem. This problem consists...
Vahab S. Mirrokni, Shayan Oveis Gharan, Morteza Za...
In this paper we introduce a new algorithm for model order reduction in the presence of parameter or process variation. Our analysis is performed using a graph interpretation of t...
Curvature flows have been extensively considered from a deterministic point of view. They have been shown to be useful for a number of applications including crystal growth, flame...
Gozde B. Unal, Delphine Nain, G. Ben-Arous, Nahum ...
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...