Optimizing network robustness via Krylov subspaces

Stefano Massei, Francesco Tudisco

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We consider the problem of attaining either the maximal increase or reduction of the robustness of a complex network by means of a bounded modification of a subset of the edge weights. We propose two novel strategies combining Krylov subspace approximations with a greedy scheme and an interior point method employing either the Hessian or its approximation computed via the limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS). The paper discusses the computational and modeling aspects of our methodology and illustrates the various optimization problems on networks that can be addressed within the proposed framework. Finally, in the numerical experiments we compare the performances of our algorithms with state-of-the-art techniques on synthetic and real-world networks.
Original languageEnglish
Pages (from-to)131-155
Number of pages25
JournalESAIM: Mathematical Modelling and Numerical Analysis
Volume58
Issue number1
Early online date31 Jan 2024
DOIs
Publication statusPublished - 28 Feb 2024

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