How is exact line search adapted for projected gradient descent in convex optimization?
One way I think of is that unconstrained exact line search is run, and the new point is projected into the feasibility domain (constraint set). I am not sure projected gradient descent has a convergence proof in that case. Does it?
Another way can be that the constraints are considered when performing exact line search. Then I am not sure how to solve the exact line search problem.