To address the need for highly configurable and customizable distributed systems, researchers and practitioners are investigating various innovative approaches. One of the promisi...
Approximate symbolic computation problems can be formulated as constrained or unconstrained optimization problems, for example: GCD [3, 8, 12, 13, 23], factorization [5, 10], and ...
Robust Optimization is a rapidly developing methodology for handling optimization problems affected by non-stochastic "uncertain-butbounded" data perturbations. In this p...
In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimiza...