Lipschitz continuity of vector-valued function

  • #1
psie
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TL;DR Summary
I have a question about a proof on Lipschitz continuity of a vector-valued function.
I'm reading Ordinary Differential Equations by Andersson and Böiers, although this is more related to multivariable calculus. There is a Lemma regarding Lipschitz continuity which I have a question about. Below ##\pmb{f}:\mathbf{R}^{n+1}\to \mathbf{R}^n ## is a vector-valued function defined by ##\pmb{f}=\pmb{f}(t,\pmb{x})##, where ##\pmb x## is a vector in ##\mathbf{R}^n## that depends on ##t##.

Lemma. Assume that ##\Omega\subseteq \mathbf{R}\times\mathbf{R}^n## is a convex and bounded set, and that the function ##\pmb{f}## is continuously differentiable in a neighborhood of ##\overline{\Omega}##. Then ##\pmb{f}## is Lipschitz continuous in ##\Omega##.

Proof. The line segment between two points ##(t,\pmb{x})## and ##(t,\pmb{y})## in ##\Omega## is contained in ##\Omega## by the assumption of convexity. Hence \begin{align} \pmb{f}(t,\pmb{x})-\pmb{f}(t,\pmb{y})&=\int_0^1\frac{\mathrm{d}}{\mathrm{d}s}\pmb{f}(t,\pmb{y}+s(\pmb{x}-\pmb{y}))\mathrm{d}s \tag1 \\ &=\int_0^1\sum_{i=1}^n\frac{\partial \pmb{f}}{\partial x_i}(t,\pmb{y}+s(\pmb{x}-\pmb{y}))(x_i-y_i)\mathrm{d}s,\tag2\end{align} by the chain rule. Set ##K=\max_i\sup_{\Omega}\left|\frac{\partial f}{\partial x_i}\right|## (which exists by assumption). Then $$|\pmb{f}(t,\pmb{x})-\pmb{f}(t,\pmb{y})|\leq\int_0^1 nK|\pmb{x}-\pmb{y}|\mathrm{d}s=nK|\pmb{x}-\pmb{y}|.\tag3$$

Here's my understanding of the notation. In ##(2)##, ##\frac{\partial \pmb{f}}{\partial x_i}(t,\pmb{y}+s(\pmb{x}-\pmb{y}))## is a vector evaluated at ##(t,\pmb{y}+s(\pmb{x}-\pmb{y}))##. ##\frac{\partial}{\partial x_i}## denotes the partial derivative of ##\pmb{f}(t,x_1,\ldots,x_n)## with respect ##x_i## (a better notation in ##(2)## would probably be ##\frac{\partial}{\partial z_i}##, as ##\pmb{x}## is already being used to denote a vector). More precisely, if ##f_1,\ldots,f_n## are the components of ##\pmb{f}##, then $$\frac{\partial \pmb{f}}{\partial x_i}(t,\pmb{y}+s(\pmb{x}-\pmb{y}))=\begin{pmatrix}
\frac{\partial f_1}{\partial x_i}(t,\pmb{y}+s(\pmb{x}-\pmb{y})) \\ \vdots \\ \frac{\partial f_n}{\partial x_i}(t,\pmb{y}+s(\pmb{x}-\pmb{y}))
\end{pmatrix}.\tag4$$

My question is that I do not really understand how ##(3)## is obtained. For the sake of brevity, I will write ##\frac{\partial \pmb{f}}{\partial x_i}## when I mean ##\frac{\partial \pmb{f}}{\partial x_i}(t,\pmb{y}+s(\pmb{x}-\pmb{y}))##. Suppose ##n=2##. Taking the norm (##\ell^2## norm to be exact) of ##(1)## and using ##\left\lVert\int_a^b \pmb{f}\right\rVert\le\int_a^b\left\lVert\pmb{f}\right\rVert##, we get
\begin{align}
\lVert\pmb{f}(t,\pmb{x})-\pmb{f}(t,\pmb{y})\rVert_2&=\left\lVert\int_0^1\sum_{i=1}^2\frac{\partial \pmb{f}}{\partial x_i}(x_i-y_i)\mathrm{d}s\right\rVert_2 \tag5\\
&\leq \int_0^1\left\lVert\sum_{i=1}^2\frac{\partial \pmb{f}}{\partial x_i}(x_i-y_i) \right\rVert_2 \mathrm{d}s \tag6
\end{align}
Next, using the triangle inequality, the norm inside the integral gets bounded by
\begin{align}
\left\lVert\sum_{i=1}^2\frac{\partial \pmb{f}}{\partial x_i}(x_i-y_i) \right\rVert_2 &\leq\left\lVert \frac{\partial \pmb{f}}{\partial x_1}(x_1-y_1)\right\rVert_2+\left\lVert \frac{\partial \pmb{f}}{\partial x_2}(x_2-y_2)\right\rVert_2. \tag7
\end{align}
The above terms on the right-hand side equal
\begin{align}
\sqrt{\left(\frac{\partial f_1}{\partial x_1}\right)^2+\left(\frac{\partial f_2}{\partial x_1}\right)^2}|x_1-y_1|+\sqrt{\left(\frac{\partial f_1}{\partial x_2}\right)^2+\left(\frac{\partial f_2}{\partial x_2}\right)^2}|x_2-y_2|.\tag8
\end{align}
Letting ##K=\max\limits_{i,j\in\{1,2\}}\sup\limits_{\Omega}\left|\frac{\partial f_i}{\partial x_j}\right|##, ##(8)## gets bounded by
\begin{align}
\sqrt{2}K|x_1-y_1|+\sqrt{2}K|x_2-y_2|=\sqrt{2}K(|x_1-y_1|+|x_2-y_2|) \tag9
\end{align}
which is clearly not equal to ##nK|\pmb{x}-\pmb{y}|##. I suspect I am doing something wrong, but I'm unable to spot it.
 
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  • #2
A basic upper bound for the value of an integral of [itex]F: [a,b] \to \mathbb{R}[/itex] is [tex]
\int_a^b F(s)\,ds \leq (b- a)\sup_{s \in [a,b]} F(s).[/tex] By extension, given [tex]I = \int_0^1 \sum_{i=1}^n\|\mathbf{F}_i(t,s)\||x_i - y_i|\,ds[/tex] we can obtain [tex]\begin{split}
I &\leq \sum_{i=1}^n \sup_s \|\mathbf{F}_i\||x_i - y_i| \\
&\leq \left(\max_i \sup_s \|\mathbf{F}_i\|\right) \sum_{i=1}^n |x_i - y_i|.\end{split}[/tex] Now [tex]
\begin{split}
\sum_{i=1}^n |x_i - y_i| &= \left|(1, \dots, 1) \cdot (|x_1-y_1|, \dots, |x_n - y_n|)\right| \\
& \leq \| (1, \dots, 1) \| \|(|x_1-y_1|, \dots, |x_n - y_n|)\| \\
& = \sqrt{n} \|\mathbf{x} - \mathbf{y}\| \\
& \leq n \|\mathbf{x} - \mathbf{y}\|.\end{split}[/tex] so that [tex]
I \leq \left(\max_i \sup_s \|\mathbf{F}_i\|\right) \sum_{i=1}^n |x_i - y_i| \leq \left(\max_i \sup_s \|\mathbf{F}_i\|\right) n \|\mathbf{x} - \mathbf{y}\|.[/tex]
 
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  • #3
Thank you for replying.

In the statement of the lemma, the authors stipulate that ##\pmb f## be ##C^1## in a neighborhood of the closure of ##\Omega## (I interpret ##\overline{\Omega}## to be the closure of ##\Omega##). Do you by any chance know why this is required? I see why we need the function to be ##C^1##, since we are integrating its derivative, but I do not see why this condition needs to be satisfied in the closure of ##\Omega##.
 
  • #4
To guarantee boundedness of the derivatives we require that they be continuous (hence [itex]C^1[/itex]) on a compact domain. This is the generalisation of the theorem that a function continuous on a closed, bounded interval is bounded, but a function continuous on an unclosed bounded interval may not be bounded (eg. [itex]x^{-1}[/itex] on [itex](0,1][/itex]).

For the Euclidean metric on [itex]\mathbb{R}^m[/itex], a subset is compact if and only if it is closed and bounded. We are given that [itex]\Omega \subset \mathbb{R} \times \mathbb{R}^n \simeq \mathbb{R}^{n+1}[/itex] is bounded, but we are not told that it is closed; its closure, however, is certainly closed and bounded.
 
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1. What is Lipschitz continuity?

Lipschitz continuity is a mathematical concept that describes the behavior of a function. A function is considered Lipschitz continuous if there exists a constant value, called the Lipschitz constant, that bounds the ratio of the change in the output of the function to the change in the input. In simpler terms, this means that the function does not change too quickly or too erratically as the input changes.

2. What does it mean for a vector-valued function to be Lipschitz continuous?

A vector-valued function is Lipschitz continuous if each component of the function is Lipschitz continuous. This means that the Lipschitz constant for the function as a whole is equal to the maximum of the Lipschitz constants for each component.

3. How is Lipschitz continuity related to the differentiability of a function?

Lipschitz continuity is a stronger condition than differentiability. A differentiable function is also Lipschitz continuous, but the converse is not always true. This means that if a function is Lipschitz continuous, it is also differentiable, but if a function is differentiable, it may not necessarily be Lipschitz continuous.

4. Why is Lipschitz continuity important in mathematics?

Lipschitz continuity is important in mathematics because it allows us to make guarantees about the behavior of a function. Functions that are Lipschitz continuous have well-behaved derivatives, which makes them easier to work with in mathematical analysis and optimization problems. Additionally, Lipschitz continuity is a key concept in the study of differential equations and dynamical systems.

5. How is Lipschitz continuity used in real-world applications?

Lipschitz continuity has many practical applications in fields such as engineering, physics, and computer science. It is used in the analysis and design of control systems, signal processing, and optimization algorithms. In machine learning, Lipschitz continuity is used to ensure the stability and convergence of neural networks. It is also a key concept in the study of chaos theory and its applications in various fields.

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