Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of discrete optimization problems, and subsequently, machine learning and control theory problems. On the one hand, SCCs are used in solving the 2-satisfiability problem, which has applications in clustering, scheduling, and visualization. On the other hand, the diameter has applications in network learning and discovery problems enabling efficient internet routing and searches, as well as identifying faults in the power grid.In this paper, we leverage consensus-based principles to find the SCCs in a scalable and distributed fashion with a computational complexity of O Dd max in-degree , where D is the (finite) diameter of the network and d max in-degree is the maximum in-degree of the network. Additionally, we prove that our algorithm terminates in D + 1 iterations, which allows us to retrieve the diameter of the network. We illustrate the performance of our algorithm on several random networks, including Erdős-Rényi, Barabási-Albert, and Watts-Strogatz networks.
In this paper, we study the structural state and input observability of continuous-time switched linear timeinvariant systems and unknown inputs. First, we provide necessary and sufficient conditions for their structural state and input observability that can be efficiently verified in O((m(n + p)) 2 ), where n is the number of state variables, p is the number of unknown inputs, and m is the number of modes. Moreover, we address the minimum sensor placement problem for these systems by adopting a feed-forward analysis and by providing an algorithm with a computational complexity of O((m(n + p) + α) 2.373 ), where α is the number of target strongly connected components of the system's digraph representation. Lastly, we explore different assumptions on both the system and unknown inputs (latent space) dynamics that add more structure to the problem, and thereby, enable us to render algorithms with lower computational complexity, which are suitable for implementation in large-scale systems.
Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of machine learning and control theory problems. In this article, we provide for the first time a scalable distributed solution for these two problems by leveraging dynamical consensus-like protocols to find the SCCs. The proposed solution has a time complexity of O(NDd max in-degree ), where N is the number of vertices in the network, D is the (finite) diameter of the network, and d max in-degree is the maximum in-degree of the network. Additionally, we prove that our algorithm terminates in D + 2 iterations, which allows us to retrieve the finite diameter of the network. We perform exhaustive simulations that support the outperformance of our algorithm against the state of the art on several random networks, including Erd ős-Rényi, Barabási-Albert, and Watts-Strogatz networks.
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