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- W4289382425 abstract "The notion of strong structural controllability (s-controllability) allows for determining controllability properties of large linear time-invariant systems even when numerical values of the system parameters are not known a priori. The s-controllability guarantees controllability for all numerical realizations of the system parameters. We address the optimization problem of minimal cardinality input selection for s-controllability. Previous work shows that not only the optimization problem is NP-hard, but finding an approximate solution is also hard. We propose a randomized algorithm using the notion of zero forcing sets to obtain an optimal solution with high probability. We compare the performance of the proposed algorithm with a known heuristic [1] for synthetic random systems and five real-world networks, viz. IEEE 39-bus system, re-tweet network, protein-protein interaction network, US airport network, and a network of physicians. It is found that our algorithm performs much better than the heuristic in each of these cases." @default.
- W4289382425 created "2022-08-02" @default.
- W4289382425 creator A5011685597 @default.
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- W4289382425 date "2018-10-16" @default.
- W4289382425 modified "2023-09-24" @default.
- W4289382425 title "Minimizing Inputs for Strong Structural Controllability" @default.
- W4289382425 doi "https://doi.org/10.48550/arxiv.1810.07220" @default.
- W4289382425 hasPublicationYear "2018" @default.
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