Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Abstract— In this paper we consider several problems involving control with limited actuation and sampling rates. Event-based control has emerged as an attractive approach for ad...
We present a fast method that adaptively approximates large-scale functional scattered data sets with hierarchical B-splines. The scheme is memory efficient, easy to implement an...
Our main goal in this paper is to study the scheduling of parallel BSP tasks on clusters of computers. We focus our attention on special characteristics of BSP tasks, which can use...
In this paper we present an algorithm for scheduling parallel applications that consist of a divisible workload. Our algorithm uses multiple rounds to overlap communication and co...