We introduce a stochastic framework into the open--source Core Imaging
Library (CIL) which enables easy development of stochastic algorithms. Five
such algorithms from the literature are developed, Stochastic Gradient Descent,
Stochastic Average Gradient (-Am\'elior\'e), (Loopless) Stochastic Variance
Reduced Gradient. We showcase the functionality of the framework with a
comparative study against a deterministic algorithm on a simulated 2D PET
dataset, with the use of the open-source Synergistic Image Reconstruction
Framework. We observe that stochastic optimisation methods can converge in
fewer passes of the data than a standard deterministic algorithm.

我们在开源的 Core Imaging Library (CIL) 中引入了一个随机框架，使得随机算法的开发变得更加容易。我们从文献中开发了五种这样的算法：随机梯度下降法、改良的随机平均梯度法、去循环的随机方差减少梯度法。通过对一个模拟的二维 PET 数据集进行与确定性算法的对比研究，我们展示了该框架的功能性。我们观察到，与标准的确定性算法相比，随机优化方法可以在更少的数据遍历次数内收敛。