Robust Optimization is a rapidly developing methodology for handling optimization problems affected by non-stochastic "uncertain-butbounded" data perturbations. In this p...
State-of-the-art linear programming (LP) solvers give solutions without any warranty. Solutions are not guaranteed to be optimal or even close to optimal. Of course, it is general...
Marcel Dhiflaoui, Stefan Funke, Carsten Kwappik, K...
Abstract--We consider the problem of linear zero-forcing precoding design and discuss its relation to the theory of generalized inverses in linear algebra. Special attention is giv...
Background: Baum-Welch training is an expectation-maximisation algorithm for training the emission and transition probabilities of hidden Markov models in a fully automated way. I...
We investigate network design under volatile conditions of link failures and traffic overload. Our model is a non-simultaneous 2-commodity problem. We characterize the feasible so...