论文标题
大规模基础设施网络的可扩展原始分解方案
Scalable Primal Decomposition Schemes for Large-Scale Infrastructure Networks
论文作者
论文摘要
大规模基础设施网络的运行需要可扩展的优化方案。为了确保安全的系统操作，少量迭代的高度可行性很重要。分解方案可以帮助实现可伸缩性。但是，就可行性而言，经典方法（例如乘数的交替方向方法（ADMM））通常会缓慢收敛。在这项工作中，我们提出了针对层次结构的强烈凸出QP的原始分解方案。这些方案在少数迭代中提供了高度的可行性，并结合全球融合保证。我们对集中式现成的内部求解器IPOPT和ADMM进行基准的性能，讨论了最多300,000个决策变量和约束的问题。我们发现所提出的方法可以像iPopt一样快地解决问题，但是沟通减少并且不需要完整的模型交换。此外，提议的方案比ADMM获得更高的精度。
The operation of large-scale infrastructure networks requires scalable optimization schemes. To guarantee safe system operation, a high degree of feasibility in a small number of iterations is important. Decomposition schemes can help to achieve scalability. In terms of feasibility, however, classical approaches such as the alternating direction method of multipliers (ADMM) often converge slowly. In this work, we present primal decomposition schemes for hierarchically structured strongly convex QPs. These schemes offer high degrees of feasibility in a small number of iterations in combination with global convergence guarantees. We benchmark their performance against the centralized off-the-shelf interior-point solver Ipopt and ADMM on problems with up to 300,000 decision variables and constraints. We find that the proposed approaches solve problems as fast as Ipopt, but with reduced communication and without requiring a full model exchange. Moreover, the proposed schemes achieve a higher accuracy than ADMM.
