Compared to the gradient descent algorithm, the Frank-Wolfe alogrithm does not take a step in the direction of the gradient, but in the direction of the point that minimizes the linear approximation of the function f over the convex set D (see Fig 3).
this ensures that the points z t always stay inside the convex set, and there is thus no need for a projection step.