What is the main characteristic of the RMSProp optimization algorithm?

The main characteristic of the RMSProp optimization algorithm is its ability to adapt the learning rate based on the magnitude of the recent gradients. This method involves maintaining a moving average of the squares of past gradients, which allows the algorithm to adjust the learning rate dynamically. When the gradients are large, the learning rate is decreased, and when the gradients are small, the learning rate is increased. This adaptive approach helps stabilize the optimization process, especially in the presence of noisy or varying gradients, ultimately leading to improved convergence properties in training neural networks.

The RMSProp algorithm is particularly beneficial in dealing with the challenges posed by non-stationary objectives, as it helps mitigate issues related to the selection of an appropriate static learning rate. This makes it especially effective in scenarios involving complex loss surfaces and diverse training datasets.