Which of the following statements about coordinate descent is true? (Select all that apply.)Group of answer choicesTo test the convergence of coordinate descent, look at the size of the maximum step you take as you cycle through coordinates.Coordinate descent cannot be used to optimize the ordinary least squares objective.Coordinate descent is always less efficient than gradient descent, but is often easier to implement.A small enough step size should be chosen to guarantee convergence.

"To test the convergence of coordinate descent, look at the size of the maximum step you take as you cycle through coordinates." - This statement is true. The convergence of coordinate descent can be tested by looking at the size of the maximum step taken as you cycle through coordinates. If the step size is decreasing and tends towards zero, it indicates that the method is converging.

"Coordinate descent cannot be used to optimize the ordinary least squares objective." - This statement is false. Coordinate descent can indeed be used to optimize the ordinary least squares objective. It is a popular method for solving large-scale problems in machine learning and statistics, including ordinary least squares.

"Coordinate descent is always less efficient than gradient descent, but is often easier to implement." - This statement is not necessarily true. The efficiency of coordinate descent compared to gradient descent can depend on the specific problem and implementation. In some cases, coordinate descent can be more efficient than gradient descent. However, it is often true that coordinate descent is easier to implement.

"A small enough step size should be chosen to guarantee convergence." - This statement is true. Choosing a small enough step size can help to ensure the convergence of the method. However, if the step size is too small, the method may converge slowly. Therefore, it's important to choose an appropriate step size.