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Stability and steady state of complex cooperative systems: a diakoptic approach.

Philip GreulichBen D MacArthurCristina PariginiRubén J Sánchez-García
Published in: Royal Society open science (2019)
Cooperative dynamics are common in ecology and population dynamics. However, their commonly high degree of complexity with a large number of coupled degrees of freedom renders them difficult to analyse. Here, we present a graph-theoretical criterion, via a diakoptic approach (divide-and-conquer) to determine a cooperative system's stability by decomposing the system's dependence graph into its strongly connected components (SCCs). In particular, we show that a linear cooperative system is Lyapunov stable if the SCCs of the associated dependence graph all have non-positive dominant eigenvalues, and if no SCCs which have dominant eigenvalue zero are connected by a path.
Keyphrases
  • convolutional neural network
  • neural network