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The convergence of the Frank–Wolfe algorithm is sublinear in general: the error to the optimum is <math>O(1/k)</math> after ''k'' iterations. The same convergence rate can also be shown if the sub-problems are only solved approximately.<ref>{{cite doi|10.1016/0022-247X(78)90137-3|noedit}}</ref>
The iterates of the algorithm can always be represented as a sparse convex combination of the extreme points of the feasible set, which has helped to the popularity of the algorithm for sparse greedy optimization in [[machine learning]] and [[signal processing]] problems,<ref>{{cite doi|10.1145/1824777.1824783|noedit}}</ref> as well as for example the optimization of [[flow network|minimum–cost flow]]s in [[Transport network|transportation network]]s.<ref>{{cite doi|10.1016/0191-2615(84)90029-8|noedit}}</ref>
If the feasible set is given by a set of linear constraints, then the subproblem to be solved in each iteration becomes a [[linear programming|linear program]].
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