Creates new instance of gradient descent optimizer. It uses Backtracking Line Search to find the good value of learning-rate (alpha) to allows it converge faster

To disable backtracking line search, simply set the learning-rate-update-rate to 1.0

Learning-rate-update-rate must be between (0, 1]

Cost function must returns both gradients and cost value

{:cost 1.5142
 :gradients [1.2 -0.5]}

Uses backtrack line search algorithm to find the best learning-rate (alpha) value.

Backtrack will stop if either of the following conditions are met:

• Cost of new theta (updated theta - i.e. theta is updated with alpha and gradients) is less or equals to cost of the original theta minus gradient length squared with alpha
• Approximately alpha reaches almost zero (1e-10)
• Approximately all gradients almost zero (1e-10)

Reference: Gradient Descent Revisited

Updates the thetas (weight) based on the provided gradients and alpha. Gradients must be the same dimension as thetas