In the context of optimization problems, what is a key benefit of using a non$-$convex function in neural network training despite the increased difficulty in finding the global minimum?

$$ It simplifies the computational complexity.

It allows for capturing more complex patterns.

It ensures faster convergence times.

$$ It reduces the need for regularization.