The '''Frank–Wolfe algorithm''' is ana simple [[iterative method|iterative]] [[First-order approximation|first-order]] [[Mathematical optimization|optimization]] [[algorithm]] for [[constrained optimization|constrained]] [[convex optimization]]. Also known as the '''conditional gradient method''',<ref>{{Cite doi|10.1016/0041-5553(66)90114-5}}</ref> '''reduced gradient algorithm''' and the '''convex combination algorithm''', the method was originally proposed by [[Marguerite Frank]] and [[Philip Wolfe]] in 1956.<ref>{{cite journal |last1=Frank |first1=M. |last2=Wolfe |first2=P. |title=An algorithm for quadratic programming |journal=Naval Research Logistics Quarterly |volume=3 |number=1-2 |year=1956 |pages=95–110|doi=10.1002/nav.3800030109}}</ref> In each iteration, the Frank–Wolfe algorithm considers a [[linear approximation]] of the objective function, and moves slightly towards a minimizer of this linear function (taken over the same ___domain).
