The aim of stochastic programming is to find optimal decisions in problems which involve
uncertain data. This field is currently develo** rapidly with contributions from many …

Many problems in artificial intelligence require adaptively making a sequence of decisions
with uncertain outcomes under partial observability. Solving such stochastic optimization …

Over the last two decades, robust optimization has emerged as a computationally attractive
approach to formulate and solve single-stage decision problems affected by uncertainty …

The invariant differential cross section for inclusive neutral-pion production in p+ p collisions
at s= 200 G e V has been measured at midrapidity (| η|< 0.35) over the range 1< p T≲ 14 G e …

We consider a discrete-time time-varying linear dynamical system, perturbed by process
noise, with linear noise corrupted measurements, over a finite horizon. We address the …

Stochastic optimization problems provide a means to model uncertainty in the input data
where the uncertainty is modeled by a probability distribution over the possible realizations …

We consider optimization problems that can be formulated as minimizing the cost of a
feasible solution w T x over an arbitrary combinatorial feasible set F⊂{0,1\}^n. For these …

We study the classical multiperiod capacitated stochastic inventory control problems in a
data-driven setting. Instead of assuming full knowledge of the demand distributions, we …

Kidneys are the most prevalent organ transplants, but demand dwarfs supply. Kidney
exchanges enable willing but incompatible donor-patient pairs to swap donors. These …

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