DCDS-B
Efficient computation of Lyapunov functions for Morse decompositions
Arnaud Goullet Shaun Harker Konstantin Mischaikow William D. Kalies Dinesh Kasti
Discrete & Continuous Dynamical Systems - B 2015, 20(8): 2419-2451 doi: 10.3934/dcdsb.2015.20.2419
We present an efficient algorithm for constructing piecewise constant Lyapunov functions for dynamics generated by a continuous nonlinear map defined on a compact metric space. We provide a memory efficient data structure for storing nonuniform grids on which the Lyapunov function is defined and give bounds on the complexity of the algorithm for both time and memory. We prove that if the diameters of the grid elements go to zero, then the sequence of piecewise constant Lyapunov functions generated by our algorithm converge to a continuous Lyapunov function for the dynamics generated the nonlinear map. We conclude by applying these techniques to two problems from population biology.
keywords: combinatorial dynamics Morse decomposition Conley's decomposition theorem algorithms. Lyapunov function
JCD
Lattice structures for attractors I
William D. Kalies Konstantin Mischaikow Robert C.A.M. Vandervorst
Journal of Computational Dynamics 2014, 1(2): 307-338 doi: 10.3934/jcd.2014.1.307
We describe the basic lattice structures of attractors and repellers in dynamical systems. The structure of distributive lattices allows for an algebraic treatment of gradient-like dynamics in general dynamical systems, both invertible and noninvertible. We separate those properties which rely solely on algebraic structures from those that require some topological arguments, in order to lay a foundation for the development of algorithms to manipulate these structures computationally.
keywords: invariant set Birkhoff's representation theorem. Attractor attracting neighborhood distributive lattice
JCD
Reconstructing functions from random samples
Steve Ferry Konstantin Mischaikow Vidit Nanda
Journal of Computational Dynamics 2014, 1(2): 233-248 doi: 10.3934/jcd.2014.1.233
From a sufficiently large point sample lying on a compact Riemannian submanifold of Euclidean space, one can construct a simplicial complex which is homotopy-equivalent to that manifold with high confidence. We describe a corresponding result for a Lipschitz-continuous function between two such manifolds. That is, we outline the construction of a simplicial map which recovers the induced maps on homotopy and homology groups with high confidence using only finite sampled data from the domain and range, as well as knowledge of the image of every point sampled from the domain. We provide explicit bounds on the size of the point samples required for such reconstruction in terms of intrinsic properties of the domain, the co-domain and the function. This reconstruction is robust to certain types of bounded sampling and evaluation noise.
keywords: nonlinear maps Homology homotopy topological inference.

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