In this paper, we propose an RADI-type method for large-scale stochastic continuous-time algebraic Riccati equations with sparse and low-rank structures. The so-called ISC method is developed by using the Incorporation idea together with different Shifts to accelerate the convergence and Compressions to reduce the storage and complexity. Numerical experiments are given to show its efficiency.

翻译：本文针对具有稀疏和低秩结构的大规模随机连续时间代数Riccati方程，提出一种RADI型方法。所提出的ISC方法通过采用结合不同移位的引入思想以加速收敛，并利用压缩技术降低存储与计算复杂度。数值实验验证了该方法的有效性。