Distributed online optimization and online games have been increasingly researched in the
last decade, mostly motivated by their wide applications in sensor networks, robotics (eg …

While methods for optimization under uncertainty have been studied intensely over the past
decades, the explicit consideration of the interplay between uncertainty and time has gained …

Machine-learned predictors, although achieving very good results for inputs resembling
training data, cannot possibly provide perfect predictions in all situations. Still, decision …

Power consumption imposes a significant cost for data centers implementing cloud services,
yet much of that power is used to maintain excess service capacity during periods of low …

This paper provides a novel technique for the analysis of randomized algorithms for
optimization problems on metric spaces, by relating the randomized performance ratio for …

An on-line problem is one in which an algorithm must handle a sequence of requests,
satisfying each request without knowledge of the future requests. Examples of on-line …

We study the complexity of finding the values and optimal strategies of mean payoff games
on graphs, a family of perfect information games introduced by Ehrenfeucht and Mycielski …

We study several versions of the shortest-path problem when the map is not known in
advance, but is specified dynamically. We are seeking dynamic decision rules that optimize …

The primal–dual method is a powerful algorithmic technique that has proved to be extremely
useful for a wide variety of problems in the area of approximation algorithms for NP-hard …

In this paper we initiate a new area of study dealing with the best way to search a possibly
unbounded region for an object. The model for our search algorithms is that we must pay …