LAB 1: DISCRETE MAPS AND NUMERICAL CHAOS
 
Mathematics: 
The
discrete logistic equation takes the form: xk+1 = r xk
(1 – xk), where  xk
xk  is a sequence of
numbers for k = 1,2,3,…,n in the unit interval: xk
 is a sequence of
numbers for k = 1,2,3,…,n in the unit interval: xk  (0,1); and r is a bifurcation
parameter: r
(0,1); and r is a bifurcation
parameter: r  (0,4). If x1
(0,4). If x1  (0,1), then the sequence of xk stays
always in the unit interval, iff r
(0,1), then the sequence of xk stays
always in the unit interval, iff r  (0,4).
(0,4).
 
 
Fixed
points x* are defined by the limit: limk
->  xk = x*.
xk = x*.
For
0 < r < 1, the only fixed point is x* = 0,
and the sequence  xk
xk  converges to  x* = 0.
 converges to  x* = 0.  
For
1 < r < 4, there are two fixed points: x*
= 0 and x* = (r-1)/r. When 1  r
 r  3, the
sequence
 3, the
sequence  xk
xk  converges to x*
= (r-1)/r. However, when 3 < r < 4, the sequence
 converges to x*
= (r-1)/r. However, when 3 < r < 4, the sequence  xk
xk  converges to 2-periodic,
4-periodic, multi-periodic, and, ultimately, to chaotic sequences of numbers in
the unit interval.
 converges to 2-periodic,
4-periodic, multi-periodic, and, ultimately, to chaotic sequences of numbers in
the unit interval. 
 
Numerical
chaos in
mathematical sense was introduced to describe the behavior of deterministic
systems (such as discrete mappings) that generate a random sequence of numbers.
For a chaotic sequence  xk
xk  , a small change in a starting value x1
results in a huge difference in a value xk for large k
> 1, i.e. any error is magnified exponentially. As a result of this
sensitivity to the starting conditions, the detailed long-term behavior of the
deterministic system becomes unpredictable whenever numerical chaos occurs.
, a small change in a starting value x1
results in a huge difference in a value xk for large k
> 1, i.e. any error is magnified exponentially. As a result of this
sensitivity to the starting conditions, the detailed long-term behavior of the
deterministic system becomes unpredictable whenever numerical chaos occurs. 
 
Objectives:
·        
visualize
various sequences of numbers  xk
xk  generated from x1
for different values of r
  generated from x1
for different values of r
·        
construct
a bifurcation diagram displaying various regimes of the discrete map for
different values of r
·        
understand
the difference between fixed points, periodic sequences, and chaotic sequences 
·        
understand
the sensitivity of the chaotic sequences to the starting value x1
 
 
A
typical cobwebbing pattern is shown here:

 xk
xk  
   xk
xk  and save them
     in a vector.
  and save them
     in a vector. 
Exploiting
the MATLAB script:
 
 
 
 
 
 
 
 
 
 
The
discrete logistic equation exhibits the bifurcation diagram called the
period-doubling route to chaos. The fixed points become unstable and are
replaced by the 2-period, 4-period and other multi-period sequences, with
larger values of the bifurcation parameter r. For yet larger
values of r, the periodic sequences of numbers become unstable
and are replaced by the chaotic sequence of numbers. The period-doubling bifurcation
diagram looks like this:
 

 
 
 
Exploiting
the MATLAB script:
 
 
Suppose
two sequences  xk
xk  and
 and  yk
yk  are started with two
values x1 and y1, that are
close in the sense that e1 = | y1 – x1 |
is small. Define an error between the two sequences: ek
= | yk – xk |. If the error converges to zero for
large k, the stationary regime is stable and deterministic, e.g.
a fixed point x*  or a periodic sequence
 are started with two
values x1 and y1, that are
close in the sense that e1 = | y1 – x1 |
is small. Define an error between the two sequences: ek
= | yk – xk |. If the error converges to zero for
large k, the stationary regime is stable and deterministic, e.g.
a fixed point x*  or a periodic sequence  xk
xk  . If the error grows or changes unpredictably (in a unit interval),
the stationary regime is chaotic and the sequence
. If the error grows or changes unpredictably (in a unit interval),
the stationary regime is chaotic and the sequence  xk
xk  is sensitive to the
starting value x1, e.g. at this picture:
 is sensitive to the
starting value x1, e.g. at this picture:
 

 
 xk
xk  and
 and  yk
yk  
   
 
Exploiting
the MATLAB script:
Identify whether the stationary regime is
deterministic or chaotic for r = 3.4; 3.6; 3.81; 3.83; 3.85.