Fractals/Iterations of real numbers/r iterations

Dynamics:
 * real analytic unimodal dynamics

=Diagram types=

2D diagrams

 * parameter is a variable on horizontal axis
 * bifurcation diagram : P-curves ( = periodic points) versus parameter
 * orbit diagram : points of critical orbit versus parameter
 * skeleton diagram ( critical curves = q-curves)
 * Lyapunow diagram : Lyapunov exponent versus parameter
 * multiplier diagrams : multiplier of periodic orbit versus parameter
 * constant parameter diagrams
 * cobweb diagram or a Verhulst diagram = Graphical iteration
 * Iterates versus time diagram = connected scatter graph = time series
 * invariant density diagram, histogram, the distribution of the orbit, frequency distribution = power spectrum
 * Poincare plot

Transformation
Exponential transformation of the parameter axis

3D diagrams
=Maps=
 * Due to numerical errors different implementations of the same equation can give different trajectories. For example:   and
 * Sensitivity to initial conditions: "small difference in the initial condition will produce large differences in the long-term behaviour of the system. This property is sometimes called the 'butterfly effect'."

Map types
 * doubling map

Tent map
Orbits of tent map:

Logistic map
names :
 * logistic map : $$f(x) = r x (1 - x),$$
 * logistic equation  $$x_{n+1} = f(x_n) ,$$
 * logistic difference equation  $$x_{n+1} = r x_n (1 - x_n) ,$$
 * discrete dynamical system

The logistic map is defined by a recurrence relation (  difference equation) :


 * $$x_{n+1} = r x_n (1 - x_n),$$

where :
 * $$r$$ is a given constant parameter
 * $$x_0$$ is given the initial term
 * $$x_n$$ is subsequent term determined by this relation

Do not confuse it with :
 * differential equation ( which gives continous version )

Bash code Javascript code from Khan Academy MATLAB code:

See also Lasin

Maxima CAS code /* Logistic diagram by Mario Rodriguez Riotorto using Maxima CAS draw packag */ pts:[]; for r:2.5 while r <= 4.0 step 0.001 do /* min r = 1 */ (x: 0.25,		for k:1 thru 1000 do x: r * x * (1-x), /* to remove points from image compute and do not draw it */		for k:1 thru 500 do (x: r * x * (1-x), /* compute and draw it */ pts: cons([r,x], pts))); /* save points to draw it later, re=r, im=x */ load(draw); draw2d(	terminal  = 'png,	file_name = "v",        dimensions = [1900,1300],	title      = "Bifurcation diagram, x[i+1] = r*x[i]*(1 - x[i])",	point_type = filled_circle,	point_size = 0.2,	color = black,	points(pts));

explicit solutions
The system with r=4 has the explicit solution for the nth iteration :

$$ x_n = sin^2(2^n arcsin \sqrt{x_0} ) $$

Precision
Numerical Precision in the Chaotic Regime : " the number of digits of precision which must be specified is about 0.6 of the number of iterations. Hence, to determine is x10 000, we need about 6000 digits."

"Hence it is not possible to predict the value of xn for very large n in the chaotic regime."

Better image


To show more detaile use tips by User:PAR:

"The horizontal axis is the r parameter, the vertical axis is the x variable. The image was created by forming a 1601 x 1001 array representing increments of 0.001 in r and x. A starting value of x=0.25 was used, and the map was iterated 1000 times in order to stabilize the values of x. 100,000 x -values were then calculated for each value of r and for each x value, the corresponding (x,r) pixel in the image was incremented by one. All values in a column (corresponding to a particular value of r) were then multiplied by the number of non-zero pixels in that column, in order to even out the intensities. Values above 250,000 were set to 250,000, and then the entire image was normalized to 0-255. Finally, pixels for values of r below 3.57 were darkened to increase visibility."

See also :
 * tips from learner.org

"The "problem" with pretty much all fractal-type systems, is, that, what happens at the beginning of an actually infinite iterational scheme, is often not descriptive of the long-time limit behaviour. And that's happening with the perturbed Lyapunov exponent from Mario Markus' algorithm as well. If I recall correctly, he states in his 90's article something like "let the x-value settle in". It's similar to the statement "for sufficiently large N" in math papers or in general with convergent series.   The actual value of how many skipping iterations one performs, is, however, (at least afaik) just a guess till you're satisfied with the quality of the image. In my images, I could not get rid of those artifacts in full, one example being the UFO image, where vasyan did a great job removing those spots:    vasyan's: https://fractalforums.org/index.php?action=gallery;sa=view;id=2388    my version (with spots): https://fractalforums.org/index.php?action=gallery;sa=view;id=1960" marcm200

Lyapunov exponent

 * code in Matlab
 * G. Pastor, M. Romera and F. Montoya, "A revision of the Lyapunov exponent in 1D quadratic maps", Physica D, 107 (1997), 17-22. Preprint

Invariant Measure
An invariant measure or probability density in state space

Video on Youtube

Real quadratic map


Great images by Chip Ross

For $$x_{n+1}=x_n^2-c$$, the code in MATLAB can be written as:

Maxima CAS code for drawing real quadratic map : $$f_c(z) = z^2 + c $$ :

/* based on the code by by Mario Rodriguez Riotorto */ pts:[]; for c:-2.0 while c <= 0.25 step 0.001 do                (x: 0.0,                for k:1 thru 1000 do x: x * x+c, /* to remove points from image compute and do not draw it */                for k:1 thru 500  do (x:  x * x+c, /* compute and draw it */ pts: cons([c,x], pts))); /* save points to draw it later, re=r, im=x */ load(draw); draw2d( terminal  = 'svg,        file_name = "b",        dimensions = [1900,1300],        title      = "Bifurcation diagram, x[i+1] = x[i]*x[i] +c",        point_type = filled_circle,        point_size = 0.2,        color = black,        points(pts));

Lyapunov exponent
=Zoom= =Cycles=
 * fractal forums: rendering-bifurcation-diagrams
 * n-cycles of polynomial maps by Cheng Zhang and paper: Cycles of the logistic map

=Points=
 * Misiurewicz Points
 * of the Logistic Map by the J. C. Sprott
 * M. Romera, G. Pastor and F. Montoya, "Misiurewicz points in one-dimensional quadratic maps", Physica A, 232 (1996), 517-535
 * G. Pastor, M. Romera, G. Álvarez and F. Montoya, "Misiurewicz point patterns generation in one-dimensional quadratic maps", Physica A, 292 (2001), 207-230
 * G. Pastor, M. Romera and F. Montoya, "On the calculation of Misiurewicz patterns in one-dimensional quadratic maps" Physica A, 232 (1996), 536-553. Preprint
 * Accumulation points
 * bifurcation points

=Constants=
 * Feigenbaum constants in wikipedia

=Properities=

self-similarity, scaling and renormalization

 * Feigenbaums Scaling Law For TheLogistic Map

=See also=
 * Geogebra: The Feigenbaum loci Author:a.zampa
 * oeis search:  logistic map
 * Logistic Map by B. L. Badger
 * Analytical properties of horizontal visibility graphs in the Feigenbaum scenario Bartolo Luque, Lucas Lacasa, Fernando J. Ballesteros, Alberto Robledo
 * The Quadratic Map is Topologically Conjugate to the Shift Map - Gareth Roberts
 * Numerical Errors in Logistic Map Calculation J. C. Sprott
 * Numbers and Computers by Ronald T. Kneusel, Reviewed by David S. Mazel, on 01/27/2016
 * Chaotic Modelling and Simulation Analysis of Chaotic Models, Attractors and Forms Christos H. Skiadas Charilaos Skiadas
 * delayed_logistic
 * The logistic map revisited Jerzy Ombach, Cracow, Poland
 * Structure in the Parameter Dependence of Order and Chaos for the Quadratic Map Brian R. Hunt and Edward Ott
 * Windows of periodicity scaling by Evgeny Demidov
 * in Webgl by Ricky Reusser
 * high-precision-feigenbaum-alpha-calc-using-julia by Stuart Brorson
 * Feigenbaum Tree by 3DXM Consortium
 * Pascal Yang Hui triangles and power laws_in_the_logistic_map by Carlos Velarde Alberto Robledo
 * demonstrations.wolfram : EstimatingTheFeigenbaumConstantFromAOneParameterScalingLaw
 * Egwald Mathematics: Nonlinear Dynamics: The Logistic Map and Chaos  by  Elmer G. Wiens

=software=
 * pynamical - Python package for modeling, simulating, visualizing, and animating discrete nonlinear dynamical systems and chaos
 * Chaos. Visualizations connecting chaos theory, fractals, and the logistic map! Written by Jonny Hyman, 2020

=Videos:=
 * Illustration of Chaotic Systems Using Logistic Map by Zylasable
 * Bifurcation Diagram for the Logistic Map Using R by Oxlajuj N'oj

=References=