We study the convergence of Markov Decision Processes made of a large number of objects to optimization problems on ordinary differential equations (ODE). We show that the optimal...
During the last decade, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs), have been shown to work well in practice and to po...
The canonical problem of solving a system of linear equations arises in numerous contexts in information theory, communication theory, and related fields. In this contribution, we...
Ori Shental, Danny Bickson, Paul H. Siegel, Jack K...
A Local Linear Embedding (LLE) module enhances the performance of two Evolutionary Computation (EC) algorithms employed as search tools in global optimization problems. The LLE em...
We propose a measurement-based routing algorithm to load-balance intradomain traffic along multiple paths for multiple unicast sources. Multiple paths are established using overla...