A Distributed Algorithm for Large-Scale Linearly Coupled Resource Allocation Problems with Selfish Agents
A decentralized randomized coordinate descent method is proposed to solve a large-scale linearly constrained, separable resource optimization problem with selfish agent. This method has a cheap computational cost and can guarantee an improvement of selected objective function without jeopardizing th...
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| Veröffentlicht in: | Scientific programming 2021-07, Vol.2021, p.1-12 |
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| container_title | Scientific programming |
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| creator | Yu, Dian Wang, Tongyao |
| description | A decentralized randomized coordinate descent method is proposed to solve a large-scale linearly constrained, separable resource optimization problem with selfish agent. This method has a cheap computational cost and can guarantee an improvement of selected objective function without jeopardizing the others in each iteration. The convergence rate is obtained using an alternative gap benchmark of objective value. Numerical simulations suggest that the algorithm will converge to a random point on the Pareto front. |
| doi_str_mv | 10.1155/2021/9939805 |
| format | Article |
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| ispartof | Scientific programming, 2021-07, Vol.2021, p.1-12 |
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| language | eng |
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| source | EZB-FREE-00999 freely available EZB journals; Wiley Online Library (Open Access Collection); Alma/SFX Local Collection |
| subjects | Algorithms Bandwidths Convergence Coupling Internet service providers Iterative methods Optimization Optimization techniques Quotas Resource allocation |
| title | A Distributed Algorithm for Large-Scale Linearly Coupled Resource Allocation Problems with Selfish Agents |
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