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.

中文翻译：

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具有自私代理的大规模线性耦合资源分配问题的分布式算法

提出了一种分散的随机坐标下降方法来解决具有自私代理的大规模线性约束、可分离的资源优化问题。该方法计算成本低廉，并且可以保证所选目标函数的改进，而不会在每次迭代中危及其他目标函数。收敛速度是使用目标值的替代差距基准获得的。数值模拟表明该算法将收敛到帕累托前沿上的一个随机点。