What is the role of learning rate and optimization in t-SNE?

Learning rate controls convergence speed and embedding stability in t-SNE.

t-SNE uses gradient descent to minimize KL divergence between distributions. The learning rate affects how quickly points move in embedding space. Too small leads to slow convergence; too large causes instability and scattered embeddings.