求解大规模线性时不变系统的最优H_2模型降阶问题的共轭梯度法

A Conjugated Gradient Algorithm for Optimal H_2 Model Reduction of Large Scale Dynamical Systems

针对最优H2模型降阶问题,提出了适合大规模多输入多输出系统的共轭梯度法。该方法仅需利用一阶导数信息,存储量少,计算复杂度低,且具有超线性收敛性。实验结果显示了算法的有效性。

A conjugated gradient algorithm with super-linear convergence which is suitable for the optimal 2 model reduction of the multi-input multi-output large scale dynamical systems is proposed.The proposed algorithm computes only first-order derivative of the cost function.It has low storage requirement and computational cost.Numerical example demonstrates the approximation accuracy and computational efficiency.