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We describe a new method for tuning a certain family of low-bandwidth controllers for linear time-invariant and spatially-invariant (LTSI) plants. We consider LTSI controllers with a fixed structure, ...
We consider the problem of finding a linear iteration that yields distributed averaging consensus over a network, i.e., that asymptotically computes the average of some initial values given at the nod...
The general setting we consider involves a process, iteration, or method in which the computation or communication at each step is local, determined by a given graph, and involves some parameters or c...
We present a distributed cross layer approach to controlling the network performance under various QoS requirements in interference limited systems. The interaction between the different layers of the...
We present a simple decentralized algorithm to solve optimization problems involving cooperative agents. Cooperative agents share a common objective and simultaneously pursue private goals. Furthermor...
We present a simple distributed algorithm for analyzing well-posedness and stability of a system composed of different sub-units, interconnected over an arbitrary graph. The procedure consists in solv...
We consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterati...
The focus of this paper is to develop a framework for distributed estimation via convex optimization. We deal with a network of complex sensor subsystems with local estimation and signal processing. M...
The paper considers a family of linear time-invariant and spatially-invariant (LTSI) systems that are both distributed and localized. The spatial responses of the distributed plant are localized in sp...
We develop computational bounds on performance for causal state feedback stochastic control with linear dynamics, arbitrary noise distribution, and arbitrary input constraint set. This can be very use...
Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasi...
Machine learning and statistics with very large datasets is now a topic of widespread interest, both in academia and industry. Many such tasks can be posed as convex optimization problems, so algorith...
Generalized additive models are an effective regression tool, popular in the statistics literature, that provide an automatic extension of traditional linear models to nonlinear systems. We pres...
This paper describes a general purpose method for solving convex optimization problems in a distributed computing environment. In particular, if the problem data includes a large linear operator or ma...
This paper presents a new distributed power control algorithm for ad-hoc wire- less networks in random channel environments. Previous work in this area has focused on distributed power control for ad-...

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