The controller synthesis procedure can be further optimized to improve computational efficiency and scalability to larger neural network models by leveraging advanced optimization techniques and parallel computing. One optimization strategy is to exploit the structure of the matrix inequalities and constraints to develop specialized solvers that can efficiently handle large-scale problems. Additionally, techniques such as warm-starting, iterative refinement, and distributed computing can be employed to speed up the optimization process. Furthermore, model reduction techniques can be applied to simplify the controller synthesis problem and reduce the computational burden. By combining these optimization strategies, the controller synthesis procedure can be made more efficient and scalable for larger neural network models.