Abstract
We study the problem of determining suitable investments in improving the robustness of complex systems comprising many component systems with an aim of minimizing the (time) average costs to system operators. The problem is formulated as an optimization problem that is nonconvex and challenging to solve for large systems. We propose two approaches to finding a good solution to the optimization problem: the first approach is based on a gradient method and finds a local optimizer. The second approach makes use of a convex relaxation of the original problem and provides both a lower bound on the optimal value and a feasible point. The lower bound can be used to bound the optimality gap of the solutions obtained by our methods. We provide numerical results to demonstrate the effectiveness of the proposed approaches.
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Mai, VS., La, R.J., Battou, A. (2023). Investments in Robustness of Complex Systems: Algorithm Design. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Micciche, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-031-21131-7_32
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