Publisher Summary This chapter discusses stochastic and dynamic networks and routing.
The chapter discusses priori optimization in routing, shortest paths, traveling salesman-type …

What is reinforcement learning? How is reinforcement learning different from stochastic
optimization? And finally, can it be used for applications to quantitative finance for my current …

Over the last decade, we have seen a revolution in connectivity between computers, and a
resulting paradigm shift from centralized to highly distributed systems. With massive scale …

Gossi** and broadcasting are two problems of information dissemination described for a
group of individuals connected by a communication network. In gossi** every person in …

Provides a modern mathematical approach to the design of communication networks for
graduate students, blending control, optimization, and stochastic network theories. A broad …

Suppose that one of n people knows a rumor. At the first stage, he passes the rumor to
someone chosen at random; at each stage, each person already informed (“knower”) …

The single source shortest path problem for arbitrary directed graphs with n nodes, m edges
and nonnegative edge weights can sequentially be solved using O (n· log n+ m) operations …

Social scientists use graphs to model group activities in social networks. An important
property in this context is the centrality of a vertex: the inverse of the average distance to …

Motivated by emerging need of learning algorithms for large scale networked and
decentralized systems, we introduce a distributed version of the classical stochastic Multi …

Randomized broadcast in networks<link href='#fn1'></link> Page 1 Randomized Broadcast in
Networks* Uriel Feiget, David Peiegt: Prabhakar Raghavan*, and Eli Upfall ABSTRACT In this …