If the optimization problem is convex and the learning rate gradually decreases according to an appropriate schedule, which of the following training algorithms will potentially arrive at $($or near$)$ the global optimum?

Group of answer choices

A$)$ Stochastic Gradient Descent

B$)$ Mini$-$Batch Gradient Descent

C$)$ Batch Gradient Descent

D$)$ A and B

E$)$ A and C

F$)$ B and C

G$)$ A and B and C

H$)$ Neither A nor B nor C