# One dimensional Maps

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1 Chapter 4 One dimensional Maps The ordinary differential equation studied in chapters 1-3 provide a close link to actual physical systems it is easy to believe these equations provide at least an approximate description of phenomenon in the real world. However it turns out to be hard to prove mathematical results for such systems, and numerical evolution with sufficient accuracy and over sufficient times to accumulate large amounts of data for statistical analysis remains a limitation. As a result the study of maps has played an important role in the understanding of chaos. In this chapter the study of one dimensional maps is introduced, and in the next chapter two dimensional maps are described. 4.1 Flows and Maps Flows The framework we have studied so far can be formalized as an set of ordinary differential equations U = f(u; r) (4.1) where U is a vector of phase space coordinates of dimension N, and r is a vector of control parameters. The equations are: autonomous: no time appears on the right hand side; deterministic: evolution is completely specified (by instantaneous U), there is no stochasticity in the equations; 1

2 CHAPTER 4. ONE DIMENSIONAL MAPS 2 dissipative: (for non-hamiltonian systems) N dimensional volumes in phase space shrink to a lower dimensional phase space giving attractors in phase space. The mathematical structure is that of smooth vector fields in R N, and the solution U(t), U(t 0 ) = U 0, is called a flow. This mathematical structure yields useful theorems [1], for example the Poincaré-Bendixson theorem, which tells us that the only long time asymptotic flows in two dimensions (N = 2) are fixed points, limit cycles, and homoclinic or heteroclinic orbits. One consequence is that there is no chaos in a two-dimensional phase space Maps Maps give an evolution analogous to (4.1) but with a discrete time U n+1 = F(U n ; r) (4.2) where U n is an N-dimensional vector with components U n (i) and F is a map from R N onto R N. These evolution equations are again autonomous and deterministic. The effect of the evolution on volumes in phase space is given by the Jacobean J F(i) J = det. (4.3) U (j) If J = 1 volumes are preserved under the iteration and the map is called volume or area preserving; if J<1 (on average) volumes decrease and the map is dissipative. There are a number of ways to connect maps with flows: We can integrate the flow for times nτ with τ some chosen fixed interval. The map can be the N 1 dimensional Poincaré section of an N dimensional flow. In these two cases a smooth flow gives a smooth map with a smooth, unique inverse (because we can integrate the flow backwards in time over the finite interval τ) i.e. is a diffeomorphism. We can construct a one dimensional return map as illustrated in chapter 1. In this case the map is an idealization, and need not be invertible. Alternatively, the modeling of the physical system may be most appropriate in terms of a discrete time, for example population dynamics may be best described in terms of annual populations.

3 CHAPTER 4. ONE DIMENSIONAL MAPS 3 Figure 4.1: Some One Dimensional Maps 4.2 Examples of 1-d maps Some examples of one dimensional maps are listed here and shown in figure 4.1. Many are motivated in terms of their simplicity. 1. The Lorenz map: given by plotting Zn+1 max against Zmax n where Zn max is the value of the Z coordinate at its nth maximum. The empirical form of the map) for Lorenz s parameter choice is sketched in figure 1: there is no analytic expression. 2. The tent map: for 0 a 2. { F(x) = ax x < 1 2 a(1 x) x > 1 2 (4.4)

4 CHAPTER 4. ONE DIMENSIONAL MAPS 4 3. The quadratic or logistic map for 0 a The shift map: 5. The sine map for 0 a 4. F(x) = ax (1 x) (4.5) F(x) = 2x mod 1 (4.6) F(x) = a sin(πx) (4.7) 4 It is conventional to scale variables so that the interval 0 x 1 is mapped into itself. The ranges of the control parameter a quoted are the limits for when this condition is satisfied. 4.3 Iterating 1-d maps The iteration of one dimensional maps is easy to see graphically: if we plot y = F(x) and y = x the iterations are given by successive steps between these two curves: y = F(x n ) x n+1 = y (4.8) Successive iterations from a given initial values are given by successive operations of the map F, an operation known as functional composition : x 1 = F(x 0 ) x 2 = F(x 1 ) = F(F(x 0 )). x n = F(x n 1 ) = F(F...F(x 0 )). (4.9) The (somewhat confusing) notation F 2 (x) is used for F(F(x)) i.e. the order 2 functional composition. Note this is not the square (F (x)) 2! We can study every 4th (for example) iteration of F by iterating F 4, etc.

5 CHAPTER 4. ONE DIMENSIONAL MAPS 5 It should be evident from the graphical scheme that the intersection x f of y = F(x)with y = x is a fixed point of the iteration i.e. F(x f ) = x f. (4.10) We can easily answer the question of whether an initial condition close to x f approaches the fixed point under iteration (when we call the fixed point stable) or moves away from it (unstable fixed point) by linearizing the evolution about x f : write x = x f + δx and then using a Taylor expansion with F (x) the derivative of the function so that x n+1 = x f + δx n+1 = F(x x + δx n ) F(x f ) + δx n F (x f ) (4.11) δx n+1 = F (x f )δx n (4.12) and δx n will increase on successive iterations for F (x f ) > 1. Thus the fixed point is stable for F (x f ) < 1 and is unstable for F (x f ) > 1. In the quadratic map, when the fixed point x f is stable almost all initial conditions lead to an orbit that converges to the fixed point (x = 0 and x = 1 being exceptional initial conditions). What happens when x f becomes unstable (which happens at a = 3)? For this map, for nearby values of a the orbits converge to an orbit which alternately visits two values x 1 and x 2 : this is the discrete time version of a limit cycle or periodic orbit (here period 2). The second iterate function y = F 2 yields three intersections with the line y = x. It is easy to check that at two of these the magnitude of the slope df 2 /dx is less than unity, i.e. there are two stable fixed points of F 2, and these correspond to x 1 and x 2. The third fixed point of F 2 is unstable: x f is of course an unstable fixed point of F 2. These points are illustrated in demonstrations Bifurcations in 1-d maps As the map parameter a is changed, the character of the long time solution may dramatically change, from a fixed point to a period two limit cycle for example. These changes are called bifurcations. The bifurcations that occur, and the different types of orbits, are well shown by the bifurcation map. This is constructed with the parameter a along the abscissa, and all values of x visited (after some number

6 CHAPTER 4. ONE DIMENSIONAL MAPS 6 of iterations to eliminate transients) plotted as points along the ordinate. A fixed point orbit over a range of a appears as a single curve, which splits into two curves at the bifurcation to the period 2 orbit etc. Chaotic dynamics, where the orbit visits an infinite number of points (otherwise the orbit would repeat, and therefore be periodic) appears as bands of continua of points (subject to limitations of how many points are actually plotted in the implementation). The bifurcation maps of our one dimensional maps show that even these very simple dynamical systems show an amazingly rich bifurcation structure (demonstration 3). This complexity in the quadratic map was studied by May [2] in the context of population dynamics. 4.5 Invariant measure Since we cannot expect to know the chaotic dynamics precisely, we need a statistical description. From the bifurcation map it appears that for some parameter values the iterated points cover intervals of the line with some density or probability distribution. We can use this to define the invariant measure of the attractor. We first define the measure ρ(x,x 0 ) as the density of points at x given by iterating many times from the initial point x 0, i.e. 1 ρ(x,x 0 )dx = lim N N n(x, x 0), (4.13) with n(x, x 0 ) the number of { F N (x 0 ), F N 1 (x 0 )...F(x 0 ) } in length dx about x, or perhaps 1 ρ(x,x 0 ) = lim N N N δ(x F i (x 0 )), (4.14) although in this last compact form we may need to fuzz out the delta function until after the N limit is taken, since, for example, if the attractor is a fixed point (where the measure should be concentrated at the fixed point) the orbit never exactly reaches the fixed point for any finite N. We would hope that the measure is a property of the attractor, and is independent of the choice of initial point x 0, except for a number of bad choices such as at unstable fixed points or unstable periodic orbits. Thus we would hope that ρ(x,x 0 ) is independent of x 0 for almost all x 0, i.e. except for a set of measure zero (with respect to the physical Lebesgue measure of the unit interval), and then defines the i=1

7 CHAPTER 4. ONE DIMENSIONAL MAPS 7 measure of the attractor ρ(x). By construction this measure is invariant i.e. is unchanged by iterating the map variable: if y = F(x)then ρ(y)dy = ρ(x)dx since all points in the interval dx end up in the interval dy = F (x)dx. The definition also incorporates the notion of ergodicity, i.e. time averages equal measure averages for almost all initial conditions (with respect to the initial Lebesgue measure). An alternative path, which does not make reference to a second measure for discussing the initial conditions, is to focus on the invariant measures, i.e. measures that are left unchanged by the dynamics. There are typically an infinite number of invariant measures (again, a measure concentrated on an unstable fixed point or orbit is invariant): some process is needed to select the physical measure that will presumably be useful experimentally or numerically. A physically appealing process, attributed to Kolmogorov, is to add a small amount of random noise to the dynamics, which will usually yield a unique measure, and then to let the noise strength tend to zero. Other schemes, which have advantages in constructing mathematical proofs, have also been discussed, for example see [1] which provides a useful discussion of the interplay of mathematical and physical ideas. An invariant measure that cannot be decomposed into different pieces that are themselves invariant is said to be ergodic. The ergodic theorem then tells us that time averages can be replaced by measure averages, again for almost all initial conditions, but now almost all means except for a set of measure zero with respect to the invariant measure. In certain cases the invariant measure can be constructed directly from its definition. Consider a density of points after n iterations ρ n (x). Then under iteration of the map the density evolves according to the Frobenius-Perron equation: ρ n+1 (y) = dx δ [y F(x)] ρ n (x) (4.15) which implements the idea that all points in the interval dx end up in the interval dy where y = F(x)and dy = F (x)dx. The invariant measure is given by equating ρ n and ρ n+1, and the approach to the invariant measure from an initial density ρ 0 (x) can also be studied. The invariant measure often shows considerable structure. For example the quadratic map at a = 4 has square root singularities at the endpoints: ρ(x) 1/ x (1 x), and at values of a intermediate between 0 and 1 shows a rich structure of singularities (see demonstration 4).

8 CHAPTER 4. ONE DIMENSIONAL MAPS Lyapunov exponents The idea of the instability of a fixed point can be generalized to make the idea of sensitive dependence on initial conditions more quantitative. Equation 4.12 can in fact be used for the expansion of a small separation at any x n δx n+1 = F (x n )δx n (4.16) so that the product of the derivatives at successive iterations gives us the expansion (or contraction) of the separation between the iterates of nearby points. More precisely we start with initial conditions x 0 and x 0 + ε and ask for the distance between the nth iterates, which we expect to grow as δx n = εe nλ(x 0) (4.17) where λ(x 0 ) is the Lyapunov exponent for the initial condition x 0, i.e. λ(x 0 ) = lim lim 1 n ε 0 n log F n (x 0 + ε) F n (x 0 ) ε = lim 1 n n log df n (x 0 ) d(x 0 ). (4.18) For systems with an ergodic invariant measure the limit exists and is independent of the initial condition x 0 for almost all initial conditions (e.g. not those points exactly on unstable periodic orbits), and will be denoted λ and called the Lyapunov exponent of the map. The derivative can be evaluated by the chain rule in terms of derivatives of F at the intermediate iterations df n (x 0 ) = F (x n 1 )F (x n 2 )...F (x 1 )F (x 0 ). (4.19) d(x 0 ) Thus we can compactly write λ = log F (4.20) where the average is over the iterations of the map. A positive value of λ corresponds to the difference between closely spaced initial conditions growing (on average exponentially) with iteration i.e. to sensitive dependence on initial conditions. Thus a positive Lyapunov exponent is a signature of chaos, and may be used as a defining criterion. The Lyapunov exponent of the tent map is easily calculated since F = a for all values of x. Thus λ = log a and we expect chaotic dynamics for 1 <a 2. The Lyapunov exponent λ(a) for other maps is shown in demonstrations 5-6. January 4, 2000

9 Bibliography [1] J-P. Eckmann and D. Ruelle, Rev. Mod. Phys. 57, 617 (1985) [2] R.M. May, Nature 261, 459 (1976) 9

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