  We address Stackelberg models of combinatorial congestion games (CCGs); we
aim to optimize the parameters of CCGs so that the selfish behavior of
non-atomic players attains desirable equilibria. This model is essential for
designing such social infrastructures as traffic and communication networks.
Nevertheless, computational approaches to the model have not been thoroughly
studied due to two difficulties: (I) bilevel-programming structures and (II)
the combinatorial nature of CCGs. We tackle them by carefully combining (I) the
idea of \textit{differentiable} optimization and (II) data structures called
\textit{zero-suppressed binary decision diagrams} (ZDDs), which can compactly
represent sets of combinatorial strategies. Our algorithm numerically
approximates the equilibria of CCGs, which we can differentiate with respect to
parameters of CCGs by automatic differentiation. With the resulting
derivatives, we can apply gradient-based methods to Stackelberg models of CCGs.
Our method is tailored to induce Nesterov's acceleration and can fully utilize
the empirical compactness of ZDDs. These technical advantages enable us to deal
with CCGs with a vast number of combinatorial strategies. Experiments on
real-world network design instances demonstrate the practicality of our method.

Computer Science and Game Theory;Data Structures and Algorithms;Machine Learning;Optimization and Control