Introduction Background Linear algebra plays a key role in the theory of dynamical systems, and concepts from dynamical systems allow the study, characterization and gen- eralization of many objects in linear algebra, such as similarity of matrices, eigenvalues, and (generalized) eigenspaces. The most basic form of this in- terplay can be seen as a quadratic matrix A gives rise to a discrete time dynamical system xk+1 = Axk, k = 0, 1, 2,... and to a continuous time dynamical system via the linear ordinary differential equation ˙ = Ax. The (real) Jordan form of the matrix A allows us to write the solution of the differential equation ˙ = Ax explicitly in terms of the matrix ex- ponential, and hence the properties of the solutions are intimately related to the properties of the matrix A. Vice versa, one can consider properties of a linear flow in Rd and infer characteristics of the underlying matrix A. Going one step further, matrices also define (nonlinear) systems on smooth manifolds, such as the sphere Sd−1 in Rd, the Grassmannian manifolds, the flag manifolds, or on classical (matrix) Lie groups. Again, the behavior of such systems is closely related to matrices and their properties. Since A.M. Lyapunov’s thesis [97] in 1892 it has been an intriguing prob- lem how to construct an appropriate linear algebra for time-varying systems. Note that, e.g., for stability of the solutions of ˙ = A(t)x it is not suﬃcient that for all t ∈ R the matrices A(t) have only eigenvalues with negative real part (see, e.g., Hahn [61], Chapter 62). Classical Floquet theory (see Floquet’s 1883 paper [50]) gives an elegant solution for the periodic case, but it is not immediately clear how to build a linear algebra around Lya- punov’s ‘order numbers’ (now called Lyapunov exponents) for more general time dependencies. The key idea here is to write the time dependency as a xi

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