  Differentiable optimization has received a significant amount of attention
due to its foundational role in the domain of machine learning based on neural
networks. This paper proposes a differentiable layer, named Differentiable
Frank-Wolfe Layer (DFWLayer), by rolling out the Frank-Wolfe method, a
well-known optimization algorithm which can solve constrained optimization
problems without projections and Hessian matrix computations, thus leading to
an efficient way of dealing with large-scale convex optimization problems with
norm constraints. Experimental results demonstrate that the DFWLayer not only
attains competitive accuracy in solutions and gradients but also consistently
adheres to constraints.
