Which of the following are true statements? Select all that apply.

To make gradient descent converge, we must slowly decrease α over time.

Gradient descent is guaranteed to find the global minimum for any function J(θ0,θ1).

Gradient descent can converge even if α is kept fixed. (But α cannot be too large, or else it may fail to converge.)

For the specific choice of cost function J(θ0,θ1) used in linear regression, there are no local optima (other than the global optimum).