Motivated by the allocation problem facing publishers in display advertising we formulate the
online assignment with forecast problem, a version of the online allocation problem where …

We study two-stage, finite-scenario stochastic versions of several combinatorial optimization
problems, and provide nearly tight approximation algorithms for them. Our problems range …

Minimum spanning tree problem (MSTP) has allured many researchers and practitioners
due to its varied range of applications in real world scenarios. Modelling these applications …

Uncertainty is a facet of many decision environments and might arise for various reasons,
such as unpredictable information revealed in the future, or inherent fluctuations caused by …

A large class of stochastic optimization problems can be modeled as minimizing an
objective function f that depends on a choice of a vector x∈ X, as well as on a random …

Robust optimization has traditionally focused on uncertainty in data and costs in optimization
problems to formulate models whose solutions will be optimal in the worst-case among the …

We study the complexity of geometric minimum spanning trees under a stochastic model of
input: Suppose we are given a master set of points s1, s_2,..., sn in d-dimensional Euclidean …

During the last decades, a host of efficient algorithms have been developed for solving the
minimum spanning tree problem in deterministic graphs, where the weight associated with …

Abstract Consider the following Steiner Tree leasing problem. Given a graph G=(V, E) with
root r, and a sequence of terminal sets D t⊆ V for each day t∈[T]. A feasible solution to the …

We consider a one-round two-player network pricing game, the Stackelberg Minimum
Spanning Tree game or StackMST. The game is played on a graph (representing a …