In this paper, a gradient-descent neurodynamic approach is proposed for the distributed linear programming problem with affine equality constraints. It is rigorously proved that the state solution of the proposed grad...
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ISBN:
(纸本)9783030228088;9783030228071
In this paper, a gradient-descent neurodynamic approach is proposed for the distributed linear programming problem with affine equality constraints. It is rigorously proved that the state solution of the proposed gradient-descent approach with an arbitrary initial point reaches agreement and is convergent to an optimal solution of the considered optimization problem at the same time. In the end, some numerical experiments are conducted to verify the effectiveness of the proposed gradient-descent approach.
In today's networked world, resource providers and consumers are distributed globally and locally, especially under current cloud computing environment. However, with resource constraints, optimization is necessar...
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In today's networked world, resource providers and consumers are distributed globally and locally, especially under current cloud computing environment. However, with resource constraints, optimization is necessary to ensure the best possible usage of such scarce resources. distributed linear programming (DisLP) problems allow collaborative agents to jointly maximize profits or minimize costs with a linear objective function while conforming to several shared as well as local linear constraints. Since each agent's share of the global constraints and the local constraints generally refer to its private limitations or capacities, serious privacy problems may arise if such information is revealed. While there have been some solutions raised that allow secure computation of such problems, they typically rely on inefficient protocols with enormous computation and communication cost. In this paper, we study the DisLP problems where constraints are arbitrarily partitioned and every agent privately holds a set of variables, and propose secure and extremely efficient approach based on mathematical transformation in two adversary models-semi-honest and malicious model. Specifically, we first present a secure column generation (SCG) protocol that securely solves the above DisLP problem amongst two or more agents without any private information disclosure, assuming semi-honest behavior (all agents properly follow the protocol but may be curious to derive private information from other agents). Furthermore, we discuss potential selfish actions and colluding issues in malicious model (distributed agents may corrupt the protocol to gain extra benefit) and propose an incentive compatible protocol to resolve such malicious behavior. To address the effectiveness of our protocols, we present security analysis for both adversary models as well as the communication/computation cost analysis. Finally, our experimental results validate the efficiency of our approach and demonstrate its sc
In this paper we propose a novel distributed algorithm to solve degenerate linear programs on asynchronous peer-to-peer networks with distributed information structures. We propose a distributed version of the well-kn...
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In this paper we propose a novel distributed algorithm to solve degenerate linear programs on asynchronous peer-to-peer networks with distributed information structures. We propose a distributed version of the well-known simplex algorithm for general degenerate linear programs. A network of agents, running our algorithm, will agree on a common optimal solution, even if the optimal solution is not unique, or will determine infeasibility or unboundedness of the problem. We establish how the multi-agent assignment problem can be efficiently solved by means of our distributed simplex algorithm. We provide simulations supporting the conjecture that the completion time scales linearly with the diameter of the communication graph. (C) 2012 Elsevier Ltd. All rights reserved.
This paper considers dyadic-exchange networks in which individual agents autonomously form coalitions of size two and agree on how to split a transferable utility. Valid results for this game include stable (if agents...
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This paper considers dyadic-exchange networks in which individual agents autonomously form coalitions of size two and agree on how to split a transferable utility. Valid results for this game include stable (if agents have no unilateral incentive to deviate), balanced (if matched agents obtain similar benefits from collaborating), or Nash (both stable and balanced) outcomes. We design provably correct continuous-time algorithms to find each of these classes of outcomes in a distributed way. Our algorithmic design to find Nash bargaining solutions builds on the other two algorithms by having the dynamics for finding stable outcomes feeding into the one for finding balanced ones. Our technical approach to establish convergence and robustness combines notions and tools from optimization, graph theory, nonsmooth analysis, and Lyapunov stability theory and provides a useful framework for further extensions. We illustrate our results in a wireless communication scenario where single-antenna devices have the possibility of working as 2-antenna virtual devices to improve channel capacity.
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