基于Zernike模式的随机并行梯度下降算法的收敛速率

为了加快控制变形镜进行波前整形的随机并行梯度下降（SPGD）算法的收敛速率,提高实时波前整形能力,本文利用由12阶Zernike多项式构成的畸变波前和32单元变形镜建立了仿真模型。基于Zernike多项式的单位正交性,得到了两个常数矩阵,当斯特列尔比（SR）达到0.8时,需要算法迭代660次,简化了算法的运算过程,加快了算法运行时间。通过Matlab7.8.0对6种SPGD算法进行仿真对比,结果显示：当SR要求不高时,可使用间接固定双边SPGD算法来提高收敛速度;当SR要求较高时,则应当使用间接自动双边SPGD算法。提出的算法为实际的激光整形提供了理论指导。

Convergence rate of stochastic parallel gradient descent algorithm based on Zernike mode

To speed up the convergence rate of Stochastic Parallel Gradient Descent（SPGD） algorithm that was used to control a deformable mirror for wavefront shaping and to enhance the capability of real-time wave-front shaping,a simulation model was established by using wave-front distortion described by 12 Zernike polynomials and a 32-unit deformable mirror.Two constant matrixes were obtained with the orthogonality of Zernike polynomials in a unit circle,which simplizes computations and speeds up the running time of the algorithm.After 660 iterations,the Strehl ratio is 0.8.Comparison results of 6 kinds of SPGD algorithms with Matlab7.8.0 show that indirect-fixed-bilateral SPGD algorithm can be used in the conditions of low Strehl ratio,and indirect-varied-bilateral SPGD algorithm can be used in the conditions of high Strehl ratio,which will speed up the convergence rate of SPGD algorithm and provide the theoretical guidance for laser shaping.