自适应并行梯度随机下降算法及其在相干合成中的应用

Adaptive Stochastic Parallel Gradient Descent Algorithm and Its Application in Coherent Beam Combining

介绍了随机并行梯度下降（SPGD）算法用于相干合成（CBC）的基本理论，提出了一种自适应SPGD算法。从扰动方式和运行步骤两个方面进行自适应控制，提高了算法收敛速率。数值计算结果表明：对于25路、49路和100路的激光阵列，采用自适应SPGD算法分别将收敛速率提高了36．6％，59．8％和80．2％，说明该方法在合成路数较大时优势更加明显，有望应用于大阵元激光相干合成系统中。

The fundamental theory for coherent beam combing （CBC） via using stochastic parallel gradient descent （SPGD） algorithm is introduced and an adaptive SPGD algorithm is proposed. The convergence rate is increased by adaptively controlling the perturbations and stages of the SPGD algorithm. The results show that for CBC of laser arrays with 25 channels, 49 channels and 100 channels by using adaptive SPGD algorithm, the convergence rates are increased by 36.6 %, 59.8 % and 80.2 %, respectively. This method has an advantage for CBC of laser arrays with large number of lasers.