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Proximal Dynamics in Multiagent Network Games

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2018年第4期5卷 1707-1716页

作者： Grammatico, Sergio Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands

We consider dynamics and protocols for agents seeking an equilibrium in a network game with proximal quadratic cost coupling. We adopt an operator-theoretic perspective to show global convergence to a network equilibrium, under the assumption of convex cost functions with proximal quadratic couplings, time-invariant and time-varying communication graph along with convex local constraints, and a time-invariant communication graph along with convex local constraints and separable convex coupling constraints. We show that proximal dynamics generalize opinion dynamics in social networks and are applicable to distributed tertiary control in power networks.

关键词： distributed algorithms multiagent systems networked control systems

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Rumor Spreading and Conductance

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JOURNAL OF THE ACM 2018年第4期65卷 1–21页

作者： Chierichetti, Flavio Giakkoupis, George Lattanzi, Silvio Panconesi, Alessandro Sapienza Univ Rome Comp Sci Dept Via Salaria 113 I-00198 Rome Italy INRIA IRISA Campus Univ Beaulieu F-35042 Rennes France Google Zurich Switzerland

In this article, we study the completion time of the PUSH-PULL variant of rumor spreading, also known as randomized broadcast. We show that if a network has n nodes and conductance phi then, with high probability, PUSH-PULL will deliver the message to all nodes in the graph within O(log n/phi) many communication rounds. This bound is best possible. We also give an alternative proof that the completion time of PUSH-PULL is bounded by a polynomial in log n/phi, based on graph sparsification. Although the resulting asymptotic bound is not optimal, this proof shows an interesting and, at the outset, unexpected connection between rumor spreading and graph sparsification. Finally, we show that if the degrees of the two endpoints of each edge in the network differ by at most a constant factor, then both PUSH and PULL alone attain the optimal completion time of O(log n/phi), with high probability.

关键词： Randomized broadcast gossip algorithms distributed algorithms randomized algorithms graph conductance graph sparsification

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Incentive Mechanism Design for Integrated Microgrids in Peak Ramp Minimization Problem

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IEEE TRANSACTIONS ON SMART GRID 2018年第6期9卷 5774-5785页

作者： Hung Khanh Nguyen Khodaei, Amin Han, Zhu Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Univ Denver Dept Elect & Comp Engn Denver CO 80210 USA

Due to the high levels of renewable integration into the future power system, matching supply to demand becomes much more challenging, which requires system operators to have sufficient ramping flexibility to respond to the huge demand-supply gap when renewable generation suddenly drops off. To overcome this challenge, microgrids have been advocated as a viable solution to reduce variability and uncertainty associated with renewable generation by rescheduling their energy generation and storage resources based on grid flexibility requirements. In this paper, we propose an incentive mechanism design to motivate microgrids to participate in the peak ramp minimization problem for the system. By offering reimbursement for each microgrid to deviate from the original optimal operation point, the ramping capability requirement to match supply demand can he significantly reduced. We model and analyze the economic interaction between the distribution system operator (DSO) and microgrids using the Nash bargaining theory. The Nash bargaining solution (NBS) can be obtained by solving the centralized social welfare maximization problem. However, due to the distributed topology of the power network as well as independent decision-making nature of microgrids, the centralized design is not suitable for practical implementation. Therefore, we propose two distributed algorithms to achieve the NBS using the alternating direction method of multipliers decomposition technique, which can execute in either synchronous fashion or asynchronous fashion. The simulation results demonstrate the convergence performance of the proposed distributed algorithms as well as the efficacy of our model in achieving benefits for both microgrids and the DSO.

关键词： ADMM distributed algorithms integrated microgrids Nash bargaining solution peak ramp minimization

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A stochastic approach to shortcut bridging in programmable matter

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NATURAL COMPUTING 2018年第4期17卷 723-741页

作者： Andres Arroyo, Marta Cannon, Sarah Daymude, Joshua J. Randall, Dana Richa, Andrea W. Univ Granada Granada Spain Univ Calif Berkeley Comp Sci Div Berkeley CA 94709 USA Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85281 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

In a self-organizing particle system, an abstraction of programmable matter, simple computational elements called particles with limited memory and communication self-organize to solve system-wide problems of movement, coordination, and configuration. In this paper, we consider a stochastic, distributed, local, asynchronous algorithm for shortcut bridging, in which particles self-assemble bridges over gaps that simultaneously balance minimizing the length and cost of the bridge. Army ants of the genus Eciton have been observed exhibiting a similar behavior in their foraging trails, dynamically adjusting their bridges to satisfy an efficiency trade-off using local interactions. Using techniques from Markov chain analysis, we rigorously analyze our algorithm, show it achieves a near-optimal balance between the competing factors of path length and bridge cost, and prove that it exhibits a dependence on the angle of the gap being shortcut similar to that of the ant bridges. We also present simulation results that qualitatively compare our algorithm with the army ant bridging behavior. Our work gives a plausible explanation of how convergence to globally optimal configurations can be achieved via local interactions by simple organisms (e.g., ants) with some limited computational power and access to random bits. The proposed algorithm also demonstrates the robustness of the stochastic approach to algorithms for programmable matter, as it is a surprisingly simple extension of our previous stochastic algorithm for compression.

关键词： Programmable matter Self-organizing particle systems Shortcut bridging distributed algorithms Markov chains Bio-inspired algorithms

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Engineering Resilient Collective Adaptive Systems by Self-Stabilisation

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ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2018年第2期28卷 1–28页

作者： Viroli, Mirko Audrito, Giorgio Beal, Jacob Damiani, Ferruccio Pianini, Danilo Univ Bologna Via Sacchi 3 I-47521 Cesena FC Italy Univ Turin Corso Svizzera 185 I-10149 Turin Italy Raytheon BBN Technol 10 Moulton St Cambridge MA 02138 USA

Collective adaptive systems are an emerging class of networked computational systems particularly suited for application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large-scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction) and require inherent self-adaptiveness properties to address unforeseen changes in operating conditions. In this context, it is extremely difficult (if not seemingly intractable) to engineer reusable pieces of distributed behaviour to make them provably correct and smoothly composable. Building on the field calculus, a computational model (and associated toolchain) capturing the notion of aggregate network-level computation, we address this problem with an engineering methodology coupling formal theory and computer simulation. On the one hand, functional properties are addressed by identifying the largest-to-date field calculus fragment generating self-stabilising behaviour, guaranteed to eventually attain a correct and stable final state despite any transient perturbation in state or topology and including highly reusable building blocks for information spreading, aggregation, and time evolution. On the other hand, dynamical properties are addressed by simulation, empirically evaluating the different performances that can be obtained by switching between implementations of building blocks with provably equivalent functional properties. Overall, our methodology sheds light on how to identify core building blocks of collective behaviour and how to select implementations that improve system performance while leaving overall system function and resiliency properties unchanged.

关键词： Aggregate computing field calculus collective adaptive systems self-stabilisation distributed algorithms simulation and modeling

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Smoothed analysis of dynamic networks

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distributed COMPUTING 2018年第4期31卷 273-287页

作者： Dinitz, Michael Fineman, Jeremy T. Gilbert, Seth Newport, Calvin Johns Hopkins Univ Baltimore MD USA Georgetown Univ Washington DC 20057 USA Natl Univ Singapore Singapore Singapore

In this paper, we generalize the technique of smoothed analysis to apply to distributed algorithms in dynamic networks in which the network graph can change from round to round. Whereas standard smoothed analysis studies the impact of small random perturbations of input values on algorithm performance metrics, our proposed dynamic network version of smoothed analysis studies the impact of random perturbations of the underlying changing network topologies. Similar to the original application of smoothed analysis, our goal is to study whether known strong lower bounds in these models are robust or fragile: do they withstand small (random) perturbations, or do such deviations push the graphs far enough from a precise pathological instance to enable much better performance? Fragile lower bounds are likely not relevant for real-world deployment, while robust lower bounds represent a true difficulty caused by dynamic behavior. We apply this technique to three standard dynamic network problems with known strong worst-case lower bounds: random walks, flooding, and aggregation. We prove that these bounds provide a spectrum of robustness when subjected to smoothing-some are fragile (random walks), some are moderately fragile (flooding), and some are robust (aggregation).

关键词： Smoothed analysis Dynamic networks distributed algorithms Flooding Aggregation Random walks

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Decentralized Online Learning With Kernels

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第12期66卷 3240-3255页

作者： Koppel, Alec Paternain, Santiago Richard, Cedric Ribeiro, Alejandro US Army Res Lab Computat & Informat Sci Directorate Adelphi MD 20783 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Univ Nice Sophia Antipolis CNRS Lab Lagrange Observ French RivieraUMR 7293 F-06108 Nice France

We consider multiagent stochastic optimization problems over reproducing kernel Hilbert spaces. In this setting, a network of interconnected agents aims to learn decision functions, i.e., nonlinear statistical models, that are optimal in terms of a global convex functional that aggregates data across the network, with only access to locally and sequentially observed samples. We propose solving this problem by allowing each agent to learn a local regression function while enforcing consensus constraints. We use a penalized variant of functional stochastic gradient descent operating simultaneously with low-dimensional subspace projections. These subspaces are constructed greedily by applying orthogonal matching pursuit to the sequence of kernel dictionaries and weights. By tuning the projection-induced bias, we propose an algorithm that allows each individual agent to learn, based on its locally observed data stream and message passing with its neighbors only, a regression function that is close to the globally optimal regression function. That is, we establish that with constant stepsize selections agents' functions converge to a neighborhood of the globally optimal one while satisfying the consensus constraints as the penalty parameter is increased. Moreover, the complexity of the learned regression functions is guaranteed to remain finite. On both multiclass kernel logistic regression and multiclass kernel support vector classification with data generated from class-dependent Gaussian mixture models, we observe an stable function estimation and the state-of-the-art performance for distributed online multiclass classification. Experiments on the Brodatz textures further substantiate the empirical validity of this approach.

关键词： distributed algorithms machine learning algorithms adaptive algorithms nonlinear filters artificial intelligence statistical learning

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The Power of Side-Information in Subgraph Detection

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第7期66卷 1905-1919页

作者： Kadavankandy, Arun Avrachenkov, Konstantin Cottatellucci, Laura Sundaresan, Rajesh Cent Supelec F-91190 Paris France INRIA F-06902 Valbonne France EURECOM Dept Commun Syst F-06410 Sophia Antipolis Biot France Friedrich Alexander Univ Erlangen Nurnberg Inst Digital Commun D-91054 Erlangen Germany Indian Inst Sci Bangalore 560012 Karnataka India

In this paper, we tackle the problem of hidden community detection. We consider belief propagation (BP) applied to the problem of detecting a hidden Erdos-Renyi (ER) graph embedded in a larger and sparser ER graph, in the presence of side-information. We derive two related algorithms based on BP to perform subgraph detection in the presence of two kinds of side-information. The first variant of side-information consists of a set of nodes, called cues, known to be from the subgraph. The second variant of side-information consists of a set of nodes that are cues with a given probability. It was shown in past works that BP without side-information fails to detect the subgraph correctly when a so-called effective signal-to-noise ratio parameter falls below a threshold. In contrast, in the presence of nontrivial side-information, we show that the BP algorithm achieves asymptotically zero error for any value of a suitably defined phase-transition parameter. We validate our results on synthetic datasets and a few real world networks.

关键词： Inference algorithms machine learning algorithms maximum likelihood detection detection algorithms distributed algorithms network theory (graphs) pattern analysis graphical models hidden Markov models belief propagation signal processing on graphs

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Greedy Sparse Learning Over Network

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2018年第3期4卷 424-435页

作者： Zaki, Ahmed Venkitaraman, Arun Chatterjee, Saikat Rasmussen, Lars Kildehoj KTH Royal Inst Technol Sch Elect Engn S-11428 Stockholm Sweden KTH Royal Inst Technol ACCESS Linneaus Ctr S-11428 Stockholm Sweden

In this paper, we develop a greedy algorithm for solving the problem of sparse learning over a right stochastic network in a distributed manner. The nodes iteratively estimate the sparse signal by exchanging a weighted version of their individual intermediate estimates over the network. We provide a restricted-isometry-property (RIP)-based theoretical performance guarantee in the presence of additive noise. In the absence of noise, we show that under certain conditions on the RIP-constant of measurement matrix at each node of the network, the individual node estimates collectively converge to the true sparse signal. Furthermore, we provide an upper bound on the number of iterations required by the greedy algorithm to converge. Through simulations, we also show that the practical performance of the proposed algorithm is better than other state-of-the-art distributed greedy algorithms found in the literature.

关键词： Adaptive estimation compressed sensing convergence distributed algorithms greedy algorithms

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Cooperative Localization in WSNs: A Hybrid Convex/Nonconvex Solution

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2018年第1期4卷 162-172页

作者： Piovesan, Nicola Erseghe, Tomaso Univ Padua Dipartimento Ingn Informaz I-35131 Padua Italy Ctr Tecnol Telecomunicac Catalunya Barcelona 08860 Spain

We propose an efficient solution to peer-to-peer localization in a wireless sensor network that works in two stages. At the first stage the optimization problem is relaxed into a convex problem, given in the form recently proposed by Soares et al. The convex problem is efficiently solved in a distributed way by an alternating directionmethod of multipliers approach, which provides a significant improvement in speed with respect to the original solution. In the second stage, a soft transition to the original, nonconvex, non-relaxed formulation is applied in such a way to force the solution toward a local minimum. The algorithm is built in such a way to be fully distributed, and it is tested in meaningful situations, showing its effectiveness in localization accuracy and speed of convergence, as well as its inner robustness.

关键词： Alternating direction method of multipliers decentralized estimation distributed algorithms cooperative localization maximum likelihood optimization methods wireless sensor networks

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