Abstract. We study two-stage, finite-scenario stochastic versions of several combinatorial optimization problems, and provide nearly tight approximation algorithms for them. Our pr...
Real-world networks often need to be designed under uncertainty, with only partial information and predictions of demand available at the outset of the design process. The field ...
We study approximations of optimization problems with probabilistic constraints in which the original distribution of the underlying random vector is replaced with an empirical dis...
We consider optimization problems that can be formulated as minimizing the cost of a feasible solution wT x over an arbitrary combinatorial feasible set F {0, 1}n . For these pro...
In this paper, we consider the problem of throughput maximization in an infrastructure based WLAN. We demonstrate that most of the proposed protocols though perform optimally for ...