To extend the proposed compositional approach to handle less commonly used activation functions like Leaky ReLU, which do not have closed-form solutions, we can employ numerical optimization techniques. Instead of relying on closed-form solutions for determining the best parameters λi, i ∈Zl−1, we can use numerical optimization algorithms like gradient descent or convex optimization to iteratively update the parameters to maximize the Lipschitz estimate. By treating λi as decision variables in the optimization problem, we can search for the optimal values that lead to the tightest Lipschitz bound for neural networks with non-standard activation functions. This adaptive approach would allow the algorithm to handle a wider range of activation functions, including those that do not admit closed-form solutions.