What type of optimization algorithm is characterized by high speed and uses early stopping?

The optimization algorithm known for its high speed and use of early stopping is Adagrad. This algorithm adapts the learning rate based on the parameters of the model as training progresses, which allows it to converge more quickly in many scenarios.

Adagrad's approach focuses on maintaining a separate learning rate for each parameter based on the historical gradients, effectively allowing it to adjust step sizes during optimization. This is particularly advantageous for high-dimensional problems where some parameters may need finer adjustments than others, leading to faster convergence.

Additionally, early stopping is an important regularization technique utilized alongside models optimized with Adagrad. By monitoring the performance of the model on a validation dataset, one can halt training when performance ceases to improve, thereby preventing overfitting and ensuring that the model generalizes well.

In contrast, other algorithms like Momentum SGD, RMSProp, and Adam may not be characterized as direct end-users of early stopping, or may have different primary focuses that do not emphasize the combination of high speed and early stopping as prominently as Adagrad does.