This paper presents mathematical programming techniques for solving a class of multi-sensor scheduling problems. Robust optimization problems are formulated for both deterministic ...
Nikita Boyko, Timofey Turko, Vladimir Boginski, Da...
—This paper presents a scheme in which a dedicated backup network is designed to provide protection from random link failures. Upon a link failure in the primary network, traffi...
Abstract. Based on the recent approach of Bertsimas and Sim (2004, 2003) to robust optimization in the presence of data uncertainty, we prove an easily computable and simple bound ...
In this paper, we propose a new methodology for handling optimization problems with uncertain data. With the usual Robust Optimization paradigm, one looks for the decisions ensurin...
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 survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approac...
Dimitris Bertsimas, David B. Brown, Constantine Ca...
We consider two new formulations for classification problems in the spirit of support vector machines based on robust optimization. Our formulations are designed to build in prote...
We study the viability of different robust optimization approaches to multiperiod portfolio selection. Robust optimization models treat future asset returns as uncertain coefficie...
—Robustness of optimization models for networking problems has been an under-explored area. Yet most existing algorithms for solving robust optimization problems are centralized,...
Kai Yang, Yihong Wu, Jianwei Huang, Xiaodong Wang,...