The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We present a distributed (non-Bayesian) learning algorithm for the problem of parameter estimation with Gaussian noise. The algorithm is expressed as explicit updates on the parameters of the Gaussian beliefs (i.e. means and precision). We show a convergence rate of O(1/k) with the constant term depending on the number of agents and the topology of the network. Moreover, we show almost sure convergence...
Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over the web, as well as global financial markets. Since their explicit structures are often unobservable, in order to facilitate network analytics, one generally resorts to approaches capitalizing on measurable nodal processes...
In this work, we consider distributed adaptive learning over multitask mean-square-error (MSE) networks where each agent is interested in estimating its own parameter vector, also called task, and where the tasks at neighboring agents are related according to a set of linear equality constraints. We assume that each agent knows its own cost function of its vector and the set of constraints involving...
The paper studies distributed Dictionary Learning (DL) problems where the learning task is distributed over a multi-agent network with time-varying (nonsymmetric) connectivity. This formulation is relevant, for instance, in Big Data scenarios where massive amounts of data are collected/stored in different spatial locations and it is unfeasible to aggregate and/or process all data in a fusion center,...
We study nonconvex distributed optimization in multiagent networks where the communications between nodes is modeled as a time-varying sequence of arbitrary digraphs. We introduce a novel broadcast-based distributed algorithmic framework for the (constrained) minimization of the sum of a smooth (possibly nonconvex and nonseparable) function, i.e., the agents' sum-utility, plus a convex (possibly nonsmooth...
This paper presents a simple and robust algorithm for determining a leader node in a cooperative network based on MDL (Minimum Description Length) subspace algorithm. The algorithm aims to improve the performance of the cooperating network in a spectrum sensing problem for cognitive radio. The outline of the communication and selection process is described and the SNR (signal to noise ratio) estimation...
High order networks are weighted hypergraphs collecting relationships between elements of tuples, not necessarily pairs. Valid metric distances between high order networks have been defined but they are difficult to compute when the number of nodes is large. The goal here is to find tractable approximations of these network distances. The paper does so by mapping high order networks to filtrations...
This paper studies the relationship between the topological structure of a social networks, and the information flow within it. In our recent work [7], we showed that a particular core-periphery decomposition using topological collapses has (a) structural properties desired in the decomposition, and (b) communal properties in the peripheral components. In this paper, we investigate the role of the...
This paper studies network topology inference, which is a cornerstone problem in statistical analysis of complex systems. The fresh look advocated here builds on recent advances in convex optimization and graph signal processing to identify the so-termed graph-shift operator (encoding the network topology) given only the eigenvectors of the shift. These spectral templates can be obtained, for example,...
We consider cooperative multi-agent resource sharing problems over an undirected network of agents, where only those agents connected by an edge can directly communicate. The objective is to minimize the sum of agent-specific composite convex functions subject to a conic constraint that couples agents' decisions. A distributed primal-dual algorithm is proposed to solve the saddle point formulation,...
Solar storms can induce quasi-dc geomagnetically induced current (GIC) flows in power grids, which could potentially lead to transformer damage and system stability and reliability issues. We consider the problem of designing operational GIC mitigation strategies by switching transmission lines. This topology control approach could relieve the power network from temporarily high level of GIC flows,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.