What are the model optimization techniques in Torch?

There are several model optimization techniques in Torch, such as:

1. Stochastic Gradient Descent (SGD) is one of the most commonly used optimization algorithms which updates model parameters by calculating gradients.
2. Adam optimizer: Adam is an adaptive learning rate optimization algorithm that combines momentum and adaptive learning rate adjustment methods, allowing for faster convergence to the optimal solution.
3. RMSprop optimizer: RMSprop is an adaptive learning rate algorithm that adjusts the learning rate by taking an exponentially weighted moving average of the squared gradients.
4. Adagrad optimizer: Adagrad is an algorithm that adjusts the learning rate based on the size of gradients, making it suitable for handling sparse data.
5. Adadelta optimizer: Adadelta is an adaptive learning rate algorithm that does not require manually setting the learning rate and can better handle non-stationary objective functions.
6. L-BFGS optimizer: L-BFGS is a quasi-Newton algorithm that is suitable for handling large-scale problems.
7. Momentum optimizer: Momentum is an optimization algorithm that accelerates convergence by introducing a momentum term to smooth the gradient update process.
8. Decreasing the learning rate gradually can help the model train more stably.

These optimization techniques can choose the appropriate algorithm to optimize the model based on specific circumstances.