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Average Consensus in the Presence of Delays and Dynamically Changing Directed Graph Topologies
 IEEE Transactions on Automatic Control
, 2012
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Multidimensional NewtonRaphson consensus for distributed convex optimization
"... Abstract — In this work we consider a multidimensional distributed optimization technique that is suitable for multiagents systems subject to limited communication connectivity. In particular, we consider a convex unconstrained additive problem, i.e. a case where the global convex unconstrained mult ..."
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Abstract — In this work we consider a multidimensional distributed optimization technique that is suitable for multiagents systems subject to limited communication connectivity. In particular, we consider a convex unconstrained additive problem, i.e. a case where the global convex unconstrained multidimensional cost function is given by the sum of local cost functions available only to the specific owning agents. We show how, by exploiting the separation of timescales principle, the multidimensional consensusbased strategy approximates a NewtonRaphson descent algorithm. We propose two alternative optimization strategies corresponding to approximations of the main procedure. These approximations introduce tradeoffs between the required communication bandwidth and the convergence speed/accuracy of the results. We provide analytical proofs of convergence and numerical simulations supporting the intuitions developed through the paper. Index Terms — multidimensional distributed optimization, multidimensional convex optimization, consensus algorithms, multiagent systems, NewtonRaphson methods I.
Decentralised MinimumTime Average Consensus in Digraphs
"... research for a long time parallel and distributed computation distributed optimization in sensor networks formation control of robotic networks dynamics of opinion forming 2 Finitetime algorithms are generally more desirable they converge in finitetime closedloop systems under finitetime control ..."
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research for a long time parallel and distributed computation distributed optimization in sensor networks formation control of robotic networks dynamics of opinion forming 2 Finitetime algorithms are generally more desirable they converge in finitetime closedloop systems under finitetime control usually demonstrate better disturbance rejection properties 2 / 20
Distributed quadratic programming under Asynchronous and Lossy Communications via NewtonRaphson Consensus
"... Abstract — Quadratic optimization problems appear in several interesting estimation, learning and control tasks. To solve these problems in peertopeer networks it is necessary to design distributed optimization algorithms supporting directed, asynchronous and unreliable communication. This paper ..."
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Abstract — Quadratic optimization problems appear in several interesting estimation, learning and control tasks. To solve these problems in peertopeer networks it is necessary to design distributed optimization algorithms supporting directed, asynchronous and unreliable communication. This paper addresses this requirement by extending a promising distributed convex optimization algorithm, known as NewtonRaphson consensus, and originally designed for static and undirected communication. Specifically, we modify this algorithm so that it can cope with asynchronous, broadcast and unreliable lossy links, and prove that the optimization strategy correctly converge to the global optimum when the local cost functions are quadratic. We then support the intuition that this robustified algorithm converges to the true optimum also for general convex problems with dedicated numerical simulations. I.
1Distributed Source Seeking via a Circular Formation of Agents under Communication Constraints
"... Abstract—This paper addresses the source seeking problem in which a group of autonomous vehicles must locate and follow the source of some signal based on measurements of the signal strength at different positions. Based on the observation that the gradient of the signal strength can be approximated ..."
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Abstract—This paper addresses the source seeking problem in which a group of autonomous vehicles must locate and follow the source of some signal based on measurements of the signal strength at different positions. Based on the observation that the gradient of the signal strength can be approximated by a circular formation of agents via a simple weighted average of the signal measured by each agent, we propose a combination of a cooperative control law to stabilize the agents to a circular formation and a distributed consensusbased source seeking algorithm, which is guaranteed to steer the circular formation towards the vicinity of the source location. In particular, the proposed algorithm is provided with two tunable parameters that allow for a tradeoff between speed of convergence, noise filtering and formation stability. The benefit of using consensusbased algorithms resides in a more realist discrete time control of the agents and in asynchronous communication resilient to delays, which is particularly relevant for underwater applications. The analytic results are complemented with numerical simulations. Index Terms—Distributed control, multiagent systems, source seeking, consensus algorithms, lossy communication I.
A Robust BlockJacobi Algorithm for Quadratic Programming under Lossy Communications
"... Abstract: We address the problem distributed quadratic programming under lossy communications where the global cost function is the sum of coupled local cost functions, typical in localization problems and partitionbased state estimation. We propose a novel solution based on a generalized gradient ..."
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Abstract: We address the problem distributed quadratic programming under lossy communications where the global cost function is the sum of coupled local cost functions, typical in localization problems and partitionbased state estimation. We propose a novel solution based on a generalized gradient descent strategy, namely a BlockJacobi descent algorithm, which is amenable for a distributed implementation and which is provably robust to communication failure if the step size is suciently small. Interestingly, robustness to packet loss, implies also robustness of the algorithm to broadcast communication protocols, asynchronous computation and bounded random communication delays. The theoretical analysis relies on the separation of time scales and singular perturbation theory. Our algorithm is numerically studied in the context of partitionbased state estimation in smart grids based on the IEEE 123 nodes distribution feeder benchmark. The proposed algorithm is observed to exhibit a similar convergence rate when compared with the well known ADMM algorithm with no packet losses, while it has considerably better performance when including moderate packet losses.
Asynchronous NewtonRaphson Consensus for Distributed Convex Optimization ⋆
"... Abstract: We consider the distributed unconstrained minimization of separable convex cost functions, where the global cost is given by the sum of several local and private costs, each associated to a specific agent of a given communication network. We specifically address an asynchronous distributed ..."
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Abstract: We consider the distributed unconstrained minimization of separable convex cost functions, where the global cost is given by the sum of several local and private costs, each associated to a specific agent of a given communication network. We specifically address an asynchronous distributed optimization technique called NewtonRaphson Consensus. Beside having low computational complexity, low communication requirements and being interpretable as a distributed NewtonRaphson algorithm, the technique has also the beneficial properties of requiring very little coordination and naturally supporting timevarying topologies. In this work we analytically prove that under some assumptions it shows either local or global convergence properties, and corroborate this result by the means of numerical simulations.