Nikolaos Chatzipanagiotis

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We propose a novel distributed method for convex optimization problems with a certain separability structure. The method is based on the augmented Lagrangian framework. We analyze its convergence and provide an application to two network models, as well as to a two-stage stochastic optimization problem. The proposed method compares favorably to two(More)
In this paper, we address the problem of controlling networks of wireless mobile nodes to propagate information over large distances, while minimizing power consumption and maintaining desired Quality of Service (QoS) guarantees. For this, we rely on collaborative beamforming, where groups of nodes collaborate to adjust the initial phase of their(More)
We consider a source (Alice) trying to communicate with a destination (Bob), in a way that an unauthorized node (Eve) cannot infer, based on her observations, the information that is being transmitted. The communication is assisted by multiple multi-antenna cooperating nodes (helpers) who have the ability to move. While Alice transmits, the helpers transmit(More)
We consider the scenario of a multi-cluster network, in which each cluster contains multiple single-antenna source destination pairs that communicate simultaneously over the same channel. The communications are supported by cooperating amplify-and-forward relays, which perform beamforming. While the communications take place within the cluster, there is(More)
We consider the problem of cooperative beamforming in relay networks. Assuming knowledge of the secondorder statistics of channel state information (CSI), the optimal beamforming weights are determined so that the total transmitted power at the relays is minimized, while meeting signal-to-interference-plus-noise-ratio (SINR) requirements at the(More)
In this paper we are concerned with a class of stochastic multicommodity network flow problems, the so called capacity expansion planning problems. We consider a two-stage stochastic optimization formulation that incorporates uncertainty in the problem parameters. To address the computational complexity of these stochastic models, we propose a decomposition(More)
In this paper, we propose a distributed algorithm for optimal routing in wireless multi-hop networks. We build our approach on a recently proposed model for stochastic routing, whereby each node selects a neighbor to forward a packet according to a given probability distribution. Our solution relies on dual decomposition techniques with regularization, that(More)
We consider the Accelerated Distributed Augmented Lagrangians (ADAL) algorithm, a distributed optimization algorithm that was recently developed by the authors to address problems that involve multiple agents optimizing a separable convex objective function subject to convex local constraints and linear coupling constraints. Optimization using augmented(More)