The concept of proportionally fair markets for transportation networks is studied. The goal is to find methods for flow allocation to origin/destination pairs in urban communities which is fair, efficient, and able to...
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The concept of proportionally fair markets for transportation networks is studied. The goal is to find methods for flow allocation to origin/destination pairs in urban communities which is fair, efficient, and able to dynamically adapt to the changes in origin/destinations and traffic network. Two flow markets are designed and studied. distributed and dynamic algorithms are developed to find the proportional fair allocation of flow among competing origin/destinations. Additionally, existence, uniqueness and stability of the equilibrium points are proved for both markets. Our numerical simulations supplement the stability and practicality of our proposed algorithms.
We study the role of interactivity in distributed statistical inference under information constraints, e.g., communication constraints and local differential privacy. We focus on the tasks of goodness-of-fit testing a...
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We study the role of interactivity in distributed statistical inference under information constraints, e.g., communication constraints and local differential privacy. We focus on the tasks of goodness-of-fit testing and estimation of discrete distributions. From prior work, these tasks are well understood under noninteractive protocols. Extending these approaches directly for interactive protocols is difficult due to correlations that can build due to interactivity;in fact, gaps can be found in prior claims of tight bounds of distribution estimation using interactive protocols. We propose a new approach to handle this correlation and establish a unified method to establish lower bounds for both tasks. As an application, we obtain optimal bounds for both estimation and testing under local differential privacy and communication constraints. We also provide an example of a natural testing problem where interactivity helps.
In this article, we study methods to solve a Sylvester equation in the form of AX + XB = C for given matrices A, B, C is an element of R-nxn, inspired by the distributed linear equation flows. The entries of A. B. and...
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In this article, we study methods to solve a Sylvester equation in the form of AX + XB = C for given matrices A, B, C is an element of R-nxn, inspired by the distributed linear equation flows. The entries of A. B. and C are separately partitioned into a number of pieces (or sometimes we permit these pieces to overlap), which are allocated to nodes in a network. Nodes hold a dynamic state shared among their neighbors defined from the network structure. Natural partial or full row , column partitions and block partitions of the data A, B, and C are formulated by use of the vectorized matrix equation. We show that existing network flows for distributed linear algebraic equations can be extended to solve this special form of matrix equations over networks. A "consensus + projection + symmetrization" flow is also developed for equations with symmetry constraints on the matrix variables. We prove the convergence of these flows and obtain the fastest convergence rates that these flows can achieve regardless of the choices of node interaction strengths and network structures.
This article considers a distributed Nash equilibrium seeking problem, where the players only have partial access to other players' actions, such as their neighbors' actions. Thus, the players are supposed to ...
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This article considers a distributed Nash equilibrium seeking problem, where the players only have partial access to other players' actions, such as their neighbors' actions. Thus, the players are supposed to communicate with each other to estimate other players' actions. To solve the problem, a leader-following consensus gradient-free distributed Nash equilibrium seeking algorithm is proposed. This algorithm utilizes only the measurements of the player' local cost function without the knowledge of its explicit expression or the requirement on its smoothness. Hence, the algorithm is gradient-free during the entire updating process. Moreover, the analysis on the convergence of the Nash equilibrium is studied for the algorithm with both diminishing and constant step-sizes, respectively. Specifically, in the case of diminishing step-size, it is shown that the players' actions converge to the Nash equilibrium almost surely, while in the case of fixed step-size, the convergence to the neighborhood of the Nash equilibrium is achieved. The performance of the proposed algorithm is verified through numerical simulations.
In this paper, we consider two fundamental cut approximation problems on large graphs. We prove new lower bounds for both problems that are optimal up to logarithmic factors. The first problem is to approximate cuts i...
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In this paper, we consider two fundamental cut approximation problems on large graphs. We prove new lower bounds for both problems that are optimal up to logarithmic factors. The first problem is to approximate cuts in balanced directed graphs. In this problem, the goal is to build a data structure that -approximates cut values in graphs with vertices. For arbitrary directed graphs, such a data structure requires bits even for constant . To circumvent this, recent works study -balanced graphs, meaning that for every directed cut, the total weight of edges in one direction is at most times that in the other direction. We consider two models: the model, where the goal is to approximate each cut with constant probability, and the model, where all cuts must be preserved simultaneously. We improve the previous lower bound to in the for-each model, and we improve the previous lower bound to in the for-all model. This resolves the main open questions of (Cen et al., ICALP, 2021). The second problem is to approximate the global minimum cut in a local query model, where we can only access the graph via degree, edge, and adjacency queries. We improve the previous query complexity lower bound to for this problem, where is the number of edges, is the size of the minimum cut, and we seek a -approximation. In addition, we show that existing upper bounds with slight modifications match our lower bound up to logarithmic factors.
Nowadays, Wireless Sensor Networks are one of the fundamental infrastructures for IoT technology. Although WSN has been researched for a decade, providing energy efficiency for resource-constrained sensor nodes is sti...
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Nowadays, Wireless Sensor Networks are one of the fundamental infrastructures for IoT technology. Although WSN has been researched for a decade, providing energy efficiency for resource-constrained sensor nodes is still a hot topic given the widespread usage of real-time WSN applications. For ensuring scalability, recent studies focus on multi-hop routing schemes. In this paper, a fully distributed, multi-hop intra and inter-cluster communication based static clustering scheme (MI(2)RSDiC) is proposed for WSNs. Differently from the studies in literature, MI(2)RSDiC suggests a limited re-evaluation opportunity to the nodes in clustering phase for optimized decision, an adaptive threshold-based cluster head alteration for energy efficiency and a multi-hop communication at every transmission stage for supporting large-scale WSNs. The proposed approach is compared with recent approaches and the results show that MI(2)RSDiC yields the highest lifetime of the network with achieving the least energy consumption and the largest amount of collected data among the equivalent approaches.
We design the first fully distributed algorithm for generalized Nash equilibrium seeking in aggregative games on a time-varying communication network, under partial-decision information, i.e., the agents have no direc...
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We design the first fully distributed algorithm for generalized Nash equilibrium seeking in aggregative games on a time-varying communication network, under partial-decision information, i.e., the agents have no direct access to the aggregate decision. The algorithm is derived by integrating dynamic tracking into a projected pseudo-gradient algorithm. The convergence analysis relies on the framework of monotone operator splitting and the Krasnosel'skii-Mann fixed-point iteration with errors.
This article investigates the problem of distributed cooperative energy management of multiple energy bodies with the consideration of both the optimal energy generation/consumption of each participant within single e...
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This article investigates the problem of distributed cooperative energy management of multiple energy bodies with the consideration of both the optimal energy generation/consumption of each participant within single energy body and the optimal energy distribution on the interconnected lines between any pair of energy bodies. First, we define the physical and communication structure of the system formed by many energy bodies, each of which is viewed as a multienergy prosumer. Then, a distributed energy management model is proposed to achieve not only maximum profits of overall energy generation and consumption, but also minimum cost of energy delivery. To address this issue, a distributed double-Newton descent (DDND) algorithm is proposed, which possesses two advantages. On the one hand, by employing second-order information, the concept of Newton descent is embedded into the implementation of the proposed algorithm, resulting in faster convergence speed. On the other hand, the proposed algorithm performs in a fully distributed fashion. As a consequence, each participant can locally obtain its optimal operation as well as the global energy market clearing prices;meanwhile, each energy router can locally obtain the optimal exchanged energy with its neighbor energy routers. Moreover, we prove that the proposed DDND algorithm can asymptotically converge to the global optimal point. As a result, the correctness of the DDND algorithm can be guaranteed in theory. Finally, simulation results validate the effectiveness of the proposed algorithm.
With the rapid development of the Electric Power Industrial Internet, challenges in high-concurrency data processing have become increasingly prominent, particularly issues related to data consistency, which directly ...
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distributed optimization is widely used to solve large-scale optimization problems by parallelizing gradient-based algorithms across multiple computing nodes. In asynchronous optimization, the optimization parameter i...
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distributed optimization is widely used to solve large-scale optimization problems by parallelizing gradient-based algorithms across multiple computing nodes. In asynchronous optimization, the optimization parameter is updated using stale gradients, which are gradients computed with respect to outdated parameters. Although large degrees of staleness can slow convergence, little is known about the impact of staleness and its relation to other system parameters. In this work, we analyze asynchronous optimization when implemented using either hub-and-spoke or shared memory architectures. We show that the process of gradient arrival to the master node is similar in nature to a renewal process. We derive the bandwidth requirement of the system. For the huh-and-spoke setup, we derive bounds on the expected gradient staleness and show its connection to other system parameters such as the number of workers, expected compute time, and communication delays. Our derivations reveal that it is possible to adjust gradient staleness by tuning certain parameters such as minibatch size or the n umber of workers. For the shared memory architecture, we show that the expected staleness is equivalent to the number of workers. Our derivations can be used in existing convergence analyses to express convergence rates in terms of other known system parameters. Such an expression gives further details on what factors impact convergence.
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