Is simulated annealing used in machine learning?

What is simulated annealing and how is it used in machine learning?

Simulated annealing is an optimization technique inspired by the annealing process in metallurgy. In machine learning, it's used to find optimal solutions in complex search spaces, especially for problems like hyperparameter tuning.

Can you give me an example of where simulated annealing is applied in machine learning?

Sure! One common application is in training neural networks, where it helps in optimizing the weights to minimize the loss function.

How does simulated annealing compare to other optimization methods?

Simulated annealing is particularly useful for avoiding local minima, unlike gradient descent, which can get stuck. It's slower but can yield better global solutions.

Are there any limitations to using simulated annealing?

Yes, it can be computationally expensive and may require careful tuning of parameters like the cooling schedule to be effective.