Which optimization algorithm offers good convergence and is noted for its medium speed?

The choice of Momentum SGD as an optimization algorithm noted for good convergence and medium speed is significant in the context of training deep learning models. Momentum helps accelerate gradients vectors in the right directions, thus leading to faster convergence. It does this by adding a fraction of the previous update to the current update, which mitigates the oscillations and smooths out the optimization path, making it particularly useful in areas of complex loss surfaces.

While other algorithms like Adagrad and RMSProp adapt the learning rate in a way that often leads to faster convergence initially, they can exhibit slower overall performance due to how they handle accumulated gradients. Adam is well-regarded for its fast convergence; however, its speed can sometimes lead to overshooting minima or other convergence issues if not carefully tuned.

Momentum SGD strikes a balance between speed and convergence quality, allowing for reliable advancements in training iterative models without the extreme fluctuation that some more adaptive methods can introduce, making it a suitable choice when consistent medium-speed convergence is desirable.