We describe a framework that helps students learn from examples by generating example problem solutions whose level of detail is tailored to the students' domain knowledge. T...
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...
We propose a Newton-CG primal proximal point algorithm for solving large scale log-determinant optimization problems. Our algorithm employs the essential ideas of the proximal poi...
In this paper, we provide a heuristic procedure, that performs well from a global optimality point of view, for an important and difficult class of bilevel programs. The algorithm...
Cache partitioning techniques have been proposed in the past as a solution for the cache interference problem. Due to qualitative differences with general purpose platforms, real-...
Bach Duy Bui, Marco Caccamo, Lui Sha, Joseph Marti...