论文标题
Adagrad算法的顺序收敛，以进行平滑凸优化
Sequential convergence of AdaGrad algorithm for smooth convex optimization
论文作者
论文摘要
我们证明，当用Lipschitz梯度应用于凸目标函数时，由标量步长变体或Adagrad算法的坐标变体产生的迭代序列是收敛序列。关键的见解是指出，这种Adagrad序列满足可变度量的单调性属性，该属性允许证明收敛。
We prove that the iterates produced by, either the scalar step size variant, or the coordinatewise variant of AdaGrad algorithm, are convergent sequences when applied to convex objective functions with Lipschitz gradient. The key insight is to remark that such AdaGrad sequences satisfy a variable metric quasi-Fejér monotonicity property, which allows to prove convergence.
