In power system operation, the economic dispatch problem (EDP) aims to minimize the total generation cost while meeting the demand and satisfying generator capacity limits. This paper proposes an algorithm based on th...
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In power system operation, the economic dispatch problem (EDP) aims to minimize the total generation cost while meeting the demand and satisfying generator capacity limits. This paper proposes an algorithm based on the gradient push-sum method to solve the EDP in a distributed manner over communication networks potentially with time-varying topologies and communication delays. This paper shows that the proposed algorithm is guaranteed to solve the EDP if the time-varying directed communication network is uniformly jointly strongly connected. Moreover, the proposed algorithm is also able to handle arbitrarily large but bounded time-varying delays on communication links. Numerical simulations are used to illustrate and validate the proposed algorithm.
This brief considers a distributed algorithm for solving L-1-minimization problem based on nonlinear neurodynamic system. Compared with centralized algorithms, distributed algorithms have great potential in data priva...
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This brief considers a distributed algorithm for solving L-1-minimization problem based on nonlinear neurodynamic system. Compared with centralized algorithms, distributed algorithms have great potential in data privacy protection, distributed storage and processing of data. In this brief, L-1-minimization problem is transformed into a distributed problem by using multiagent consensus theory. For the distributed optimization problem, a two-layer distributed algorithm is designed by utilizing neurodynamic system, projection matrix and derivative feedback technique. Compared with the existing distributed neurodynamic algorithm, the proposed algorithm has a simpler structure and has fewer neurons on the premise that the calculation error does not increase. Besides, the proposed algorithm converges to a minimal point of L-1-minimization problem and is Lyapunov stable. Finally, the comparative examples of sparse signal reconstruction show that the proposed distributed algorithm is effective and superior.
In this letter, a low-complexity, fully distributed algorithm is designed for dynamic channel adaptation in a canonical communication network, where each player can independently update its action without any informat...
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In this letter, a low-complexity, fully distributed algorithm is designed for dynamic channel adaptation in a canonical communication network, where each player can independently update its action without any information exchange. Both the static and dynamic channel environments are studied. The proposed algorithm converges to a set of correlated equilibria with probability one. Moreover, the optimality property of the problem is analyzed. Simulation results demonstrate that the proposed algorithm achieves a near-optimal performance for interference mitigation.
Graph semi-supervised learning (GSSL) plays an important role in data classification by leveraging the similarity across the graph topology and convex optimization with Laplacian-based regularization. However, the cur...
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Graph semi-supervised learning (GSSL) plays an important role in data classification by leveraging the similarity across the graph topology and convex optimization with Laplacian-based regularization. However, the current algorithm to solve the problem is centralized approach calling for heavy computational cost, particularly when the data is of large volume. In this paper, an innovate distributed algorithm is proposed to solve the problem, which is based on the decomposition of the similar graph. Contrary to the centralized approach, the distributed algorithm only requires the neighboring information for solving the optimization. It is proved that difference between the solutions of the distributed algorithm and centralized counterpart is upper bounded. We apply the proposed algorithm to both the synthetic and real-world datasets. The numerical results verify the effectiveness of the proposed distributed algorithm. (c) 2021 Elsevier B.V. All rights reserved.
Caching and replication of popular data objects contribute significantly to the reduction of the network bandwidth usage and the overall access time to data. Our focus is to improve the efficiency of object replicatio...
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Caching and replication of popular data objects contribute significantly to the reduction of the network bandwidth usage and the overall access time to data. Our focus is to improve the efficiency of object replication within a given distributed replication group. Such a group consists of servers that dedicate certain amount of memory for replicating objects requested by their clients. The content replication problem we are solving is defined as follows: Given the request rates for the objects and the server capacities, find the replica allocation that minimizes the access time over all servers and objects. We design a distributed approximation algorithm that solves this problem and prove that it provides a 2-approximation solution. We also show that the communication and computational complexity of the algorithm is polynomial with respect to the number of servers, the number of objects, and the sum of the capacities of all servers. Finally, we perform simulation experiments to investigate the performance of our algorithm. The experiments show that our algorithm outperforms the best existing distributed algorithm that solves the replica placement problem.
Microgrid is an effective way to accommodate distributed renewable energy, and there is a need for microgrids to participate in electricity market competition to ensure its sustainable development. For this purpose, a...
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Microgrid is an effective way to accommodate distributed renewable energy, and there is a need for microgrids to participate in electricity market competition to ensure its sustainable development. For this purpose, a market trading framework is presented where microgrids sell electricity by submitting bids in the distribution electricity market (DEM) while generators compete by submitting bids in the day-ahead wholesale market (DAWM). The retailers are considered to submit bids in the two markets to buy electricity to meet the demand of customers and an arbitrageur is introduced to buy and sell electricity between the DEM and the DAWM. Based on the market framework, a joint equilibrium model for the DEM and the DAWM is proposed. Moreover, the equilibrium problem is converted into a convex optimization problem, and the existence and uniqueness of Nash equilibrium for the DWAM and the DEM is theoretically demonstrated. Due to information asymmetry in practice, a distributed algorithm is applied to find equilibrium outcomes. Finally, numerical examples are presented to verify the effectiveness of the proposed model and algorithm.
We study the problem of allocating subchannels, bits, and powers in a cognitive radio system, in which available system resources are highly dynamic. The modulation scheme employed is orthogonal frequency-division mul...
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We study the problem of allocating subchannels, bits, and powers in a cognitive radio system, in which available system resources are highly dynamic. The modulation scheme employed is orthogonal frequency-division multiplexing (OFDM). In a resource-limited situation under which the nominal-rate requirements of users cannot be satisfied, it is desirable to provide fair degradation among users. In a situation with abundant resources, we may choose to maximize system throughput while ensuring that user nominal-rate requirements are met. The problem is formulated as a single objective nonlinear optimization problem using techniques from goal programming. A distributed resource allocation algorithm is proposed and shown to provide good fairness. In resource-abundant situations, the proposed distributed algorithm yields significantly better system throughput compared with the proportional-rate algorithm.
One of the most common ways of proving a property of invariance for a distributed algorithm is to consider the system as the product of n elementary automata, each of them being the representation of a process. The p...
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One of the most common ways of proving a property of invariance for a distributed algorithm is to consider the system as the product of n elementary automata, each of them being the representation of a process. The proof then proceeds by induction: it is assumed that the property is true for some state of the product automation, and it is proved that it remains true after each possible transition. The problem with this method is that many situations must be considered. For proving an invariance property by the demonstration by reductio ad absurdum method, the only situation where the contrary property should be true is considered and it is established that this situation cannot exist. From a combinatorial perspective, this static method results in a great simplification. This point is illustrated by applying the method to the proof of a well-known algorithm, given by Dijkstra, Feijen, and Van Gasteren (1983). This algorithm permits the detection of the termination of a distributed computation.
This paper investigates the MINimum-length--Disjoint-Paths (MIN--DP) problem: in a sensor network, given two nodes and , a positive integer , finding (node) disjoint paths connecting and with minimum total length. An ...
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This paper investigates the MINimum-length--Disjoint-Paths (MIN--DP) problem: in a sensor network, given two nodes and , a positive integer , finding (node) disjoint paths connecting and with minimum total length. An efficient distributed algorithm named Optimally-Finding-Disjoint-Paths (OFDP) is proposed for this problem. OFDP guarantees correctness and optimality, i.e., (1) it will find disjoint paths if there exist disjoint paths in the network or the maximum number of disjoint paths otherwise;(2) the disjoint paths it outputs do have minimum total length. To the best of our knowledge, OFDP is the first distributed algorithm that can solve the MIN--DP problem with correctness and optimality guarantee. Compared with the existing centralized algorithms which also guarantee correctness and optimality, OFDP is shown to be much more efficient by simulation results.
In this paper, based on an alternating direction method of multipliers (ADMM), a novel distributed algorithm is proposed to address the economic dispatch problem (EDP) in islanded microgrids. Unlike most of the existi...
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In this paper, based on an alternating direction method of multipliers (ADMM), a novel distributed algorithm is proposed to address the economic dispatch problem (EDP) in islanded microgrids. Unlike most of the existing studies that investigate the EDP with quadratic cost functions, the algorithm proposed in this paper is able to solve the EDP with general convex cost functions. Moreover, by using the center-free algorithm and the projection method, the traditional centralized ADMM is extended to the distributed implementation framework, and a fully distributed solution to the EDP is obtained. Furthermore, the proposed algorithm can deal with both equality and inequality constraints in the EDP, and the supply-demand balance can be guaranteed at any time provided that the sum of initial power outputs is equal to the total demand. The convergence property of the proposed algorithm is strictly proved. Finally, some examples are provided to further demonstrate the effectiveness of the proposed algorithm.
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