论文标题
SPOCK：一种用于多阶段风险的最佳控制问题的近端方法
SPOCK: A proximal method for multistage risk-averse optimal control problems
论文作者
论文摘要
在过去的十年中，风险规避的最佳控制问题引起了很多关注，这主要是由于它们具有吸引力的数学特性和实际重要性。它们可以看作是随机和强大的最佳控制方法之间的插值，从而使设计人员可以权衡稳健性，反之亦然。由于其随机性，规避风险的问题是非常大规模的，涉及数百万的决策变量，这在有效的计算方面构成了挑战。在这项工作中，我们提出了针对总体规避风险问题的分裂，并展示了如何在启用GPU的硬件上有效计算迭代。此外，我们提出了SPOCK-一种新算法，它利用了建议的分裂，并利用了Supermann方案以及Anderson加速方法的快速方向，以增强收敛速度。我们在朱莉娅（Julia）中实施了Spock作为开源求解器，这与温暖且大规模的并行化相提并论。
Risk-averse optimal control problems have gained a lot of attention in the last decade, mostly due to their attractive mathematical properties and practical importance. They can be seen as an interpolation between stochastic and robust optimal control approaches, allowing the designer to trade-off performance for robustness and vice-versa. Due to their stochastic nature, risk-averse problems are of a very large scale, involving millions of decision variables, which poses a challenge in terms of efficient computation. In this work, we propose a splitting for general risk-averse problems and show how to efficiently compute iterates on a GPU-enabled hardware. Moreover, we propose Spock - a new algorithm that utilizes the proposed splitting and takes advantage of the SuperMann scheme combined with fast directions from Anderson's acceleration method for enhanced convergence speed. We implement Spock in Julia as an open-source solver, which is amenable to warm-starting and massive parallelization.
