Mappings and Lyapunov/Asymptotic stability

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In summary, mappings in relation to non-linear dynamical systems can be thought of as a discrete version of vector fields. Liapunov stability for a map means that there exists a tolerance for error (epsilon) and a distance between two points (delta) such that if the points are within this delta, their distance will remain less than epsilon for all future iterations. Asymptotic stability for a map means that the distance between two points will tend to zero as the number of iterations increases.
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Hi! This is a very general question. I am trying to get my head around the concept of mappings in relation to non-linear dynamical systems. I have that
The autonomous vector field $$\dot{x} = f(x)$$,
$$x \in \bf{R}^n$$
has a fixed point
$f(\bar{x}) = 0 $

and

DEFINITION (LIAPUNOV STABILITY) $\bar{x}(t)$ is said to be stable (or
Liapunov stable) if, given $\epsilon  > 0$, there exists a $\delta = \delta(\epsilon) > 0$ such that,
for any other solution, $y(t)$, of the equation above satisfying $|x(t_o) - y(t_o)| < \delta$, then
$|\bar{x}(t) - y(t)| < \epsilon$  for $t > t_o, t_o \in  \bf{R}$

There is also a definition for asymptotic stability. At the start of the book Introduction to Applied linear dynamical systems and Chaos, by Stephen Wiggins, he talks for only a small part about maps, but then asks the reader to go about finding the definitions for liapunov and asymptotic stability for maps yourself. He also says that it is very similar to the vector field methods that he has outlined.

So I suppose what I want to know here is how I would go about describing these concepts using a mapping. I know the mapping I should be using is a bi-infinite sequence of points, I have never worked with maps up to this point so this is completely new to me. If anyone knows what the definitions are or how to go about finding them it would be a great help. It's right at the start of the book so I feel I need to get my head around this before I go further.
 
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To answer your question, you can think of a mapping in terms of a discrete version of a vector field. That is, rather than having a continuous flow of a vector field, you have a discrete set of points that are mapped from one iteration to the next. In this case, Liapunov stability for a map would mean that given some epsilon (a tolerance for error), there exists a delta (the distance between two points) such that if two points are within this delta, then their distance will remain less than epsilon for all future iterations. Asymptotic stability for a map would mean that the distance between two points will tend to zero as the number of iterations increases.

Hope this helps!
 

FAQ: Mappings and Lyapunov/Asymptotic stability

What is a mapping in the context of stability?

A mapping, also known as a dynamical system, is a mathematical model that describes the evolution of a system over time. In the context of stability, mappings are used to study the behavior of systems and determine if they will remain stable or eventually diverge.

What is Lyapunov stability?

Lyapunov stability is a property of a system that states that its trajectory remains close to an equilibrium point over time. This means that any small perturbations to the system will not cause it to deviate significantly from its stable state.

How is asymptotic stability different from Lyapunov stability?

Asymptotic stability is a stronger form of stability than Lyapunov stability. It not only guarantees that the system will remain close to an equilibrium point, but also that it will eventually converge to that point as time goes to infinity.

What is a Lyapunov function?

A Lyapunov function is a mathematical function used to prove the stability of a system. It is a scalar function that decreases over time as the system evolves, and its existence and properties can be used to show that the system is stable.

How is stability analysis used in real-world applications?

Stability analysis is used in a variety of fields, including engineering, physics, and biology, to study the behavior of systems. It is particularly useful in designing control systems for machines and predicting the long-term behavior of natural systems.

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