Lipschitz perturbations and Hammerstein integral equations

In summary, the conversation discusses a thread that derailed despite efforts to keep it on track, which frustrated the participants due to the interesting topic of elementary functional analysis. The response to the thread was massive and it is noted that Hammerstein integral equations usually arise as equivalent formulations of nonlinear boundary value problems. A simple example is given and it is shown to be equivalent to the BVP. This is a useful result as integral operators are often easier to deal with than differential operators. The conversation ends with an invitation to discuss examples of problems from physics related to BVPs.
  • #1
S.G. Janssens
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Recently I was a witness and a minor contributor to this thread, which more or less derailed, in spite of the efforts by @Samy_A. This is a pity and it angered me a bit, because the topic touches upon some interesting questions in elementary functional analysis. Here I would like to briefly discuss these questions.
 
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  • #2
The response has been so massive that it is maybe good to point out that Hammerstein integral equations usually arise rather indirectly, as equivalent formulations of nonlinear boundary value problems (BVPs). When the domain is one-dimensional (as above, where we work on the interval ##[a,b]##) these are BVPs for ordinary differential equations, whereas if the domain is multidimensional, these are BVPs for nonlinear elliptic partial differential equations. For a very simple example, consider (with ##n = 1##, ##a = 0## and ##b = 1##)
$$
\left\{
\begin{aligned}
&u''(x) + \alpha(u(x)) = \beta(x) \qquad (0 \le x \le 1)\\
& u(0) = 0 = u(1)
\end{aligned}
\qquad \text{(BVP)}
\right.
$$
where ##\alpha \in C(\mathbb{R})## and ##\beta \in C([0,1])##. If we now set
$$
k(x,y) :=
\begin{cases}
x(1 - y) &\text{if } 0 \le x \le y \le 1\\
y(1 - x) &\text{if } 0 \le y \le x \le 1
\end{cases}
$$
then ##k## is continuous on ##[a,b] \times [a,b]##. Let us also set
$$
f(x,z) := \alpha(z), \quad v(x) := -\int_0^1{k(x,y)\beta(y)\,dy} \qquad \forall\,x \in [0,1],\,\forall\,z \in \mathbb{R}
$$
With these choices for ##k##, ##f## and ##v## a little bit of calculation shows that (BVP) and (##*##) are equivalent: If ##u \in C([0,1])## is a solution of (##*##), then ##u \in C^2([0,1])## and ##u## solves (BVP) and vice versa. This is a very useful result, because integral operators are often easier to deal with than differential operators, both from a purely theoretical as well as a numerical point of view. In particular, note that
$$
\|k\|_{\infty} = \sup_{0 \le x, y \le 1}{|k(x,y)|} = \frac{1}{4}
$$
so in view of post #1 we conclude that (BVP) has a unique solution provided that ##\alpha## is Lipschitz with ##\text{Lip}(\alpha) < 4##. (In fact, we can take ##\text{Lip}(\alpha) < 8## because for this particular choice of ##k## the estimate in post #1 is easily refined.)

In these two posts, I wrote about some of the things that crossed my mind when I read through the thread I mentioned at the top of post #1. I simply didn't want these simple and classical (but beautiful, in my opinion) applications to nonlinear integral and differential equations to go unnoticed. Also, I welcome examples of problems of the type (BVP) (or of the type (##*##), for that matter) from physics. I know BVPs of this sort arise in continuum mechanics, but at present I'm not yet sufficiently well introduced to the physical aspects of that field.
 
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  • #3


Hi there, I completely agree with you. It's frustrating when a thread gets derailed, especially when the topic is something as interesting as elementary functional analysis. I think it's important for us as forum users to stay on topic and contribute to the discussion in a meaningful way. That being said, I'm still interested in hearing your thoughts on the questions that were brought up in the thread. Maybe we can start a new discussion and keep it focused on those questions?
 

Related to Lipschitz perturbations and Hammerstein integral equations

1. What are Lipschitz perturbations?

Lipschitz perturbations are a type of mathematical tool used to model and analyze systems with small changes or disturbances. They are based on the concept of Lipschitz continuity, which is a measure of how much a function can change in response to small changes in its input. In simple terms, Lipschitz perturbations describe how a system responds to minor deviations from its original state.

2. How are Lipschitz perturbations related to Hammerstein integral equations?

Lipschitz perturbations are closely related to Hammerstein integral equations because they are both used to study the behavior of dynamic systems. In particular, Hammerstein integral equations are a type of integral equation that can be solved using Lipschitz perturbations. This allows researchers to analyze the stability, convergence, and other important properties of the system.

3. What are some applications of Lipschitz perturbations and Hammerstein integral equations?

Lipschitz perturbations and Hammerstein integral equations have a wide range of applications in various fields of science and engineering. Some common applications include studying the stability of control systems, analyzing the behavior of chemical reactions, and modeling the spread of infectious diseases. They are also used in image processing, signal analysis, and many other areas.

4. How are Lipschitz perturbations and Hammerstein integral equations solved?

The process of solving Lipschitz perturbations and Hammerstein integral equations varies depending on the specific problem and the techniques used. In general, the equations are solved using computational methods such as numerical integration, iterative methods, and approximation methods. Analytical solutions are also possible in some cases, but they are usually limited to simple systems.

5. Are there any limitations or challenges associated with using Lipschitz perturbations and Hammerstein integral equations?

While Lipschitz perturbations and Hammerstein integral equations are powerful tools for analyzing dynamic systems, they do have some limitations and challenges. One of the main challenges is finding an appropriate model for the system, as well as determining the appropriate level of perturbation to use. Additionally, the computational complexity of solving these equations can be high, making them difficult to apply to large and complex systems.

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