论文标题
一种松散耦合混合整数线性编程问题的隐私感知的分布式方法
A Privacy-Aware Distributed Approach for Loosely Coupled Mixed Integer Linear Programming Problems
论文作者
论文摘要
在本文中，我们提出了两种精确的分布式算法来解决混合整数线性编程（MILP）与多种代理有关数据隐私对代理很重要的问题。一个关键的挑战是，由于MILP的非凸性性质，经典的分布式和分散优化方法不能直接应用于找到其最佳解决方案。所提出的精确算法是基于添加原始削减并限制了原始MILP问题的拉格朗日放松。我们显示了仅具有二进制和连续变量的MILP的这些算法的有限收敛。我们在单位承诺问题上测试了提出的算法，并讨论了与中央MILP方法相比的优点和缺点。
In this paper, we propose two exact distributed algorithms to solve mixed integer linear programming (MILP) problems with multiple agents where data privacy is important for the agents. A key challenge is that, because of the non-convex nature of MILPs, classical distributed and decentralized optimization approaches cannot be applied directly to find their optimal solutions. The proposed exact algorithms are based on adding primal cuts and restricting the Lagrangian relaxation of the original MILP problem. We show finite convergence of these algorithms for MILPs with only binary and continuous variables. We test the proposed algorithms on the unit commitment problem and discuss its pros and cons comparing to the central MILP approach.
