A Douglas-Rachford Splitting Method for Solving Monotone Variational Inequalities in Linear-quadratic Dynamic Games
By: Reza Rahimi Baghbadorani, Emilio Benenati, Sergio Grammatico
Potential Business Impact:
Makes self-driving cars react faster and smarter.
This paper considers constrained linear dynamic games with quadratic objective functions, which can be cast as affine variational inequalities. By leveraging the problem structure, we apply the Douglas-Rachford splitting, which generates a solution algorithm with linear convergence rate. The fast convergence of the method enables receding-horizon control architectures. Furthermore, we demonstrate that the associated VI admits a closed-form solution within a neighborhood of the attractor, thus allowing for a further reduction in computation time. Finally, we benchmark the proposed method via numerical experiments in an automated driving application.
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