Lipschitz continuity of vector-valued function

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Discussion Overview

The discussion revolves around the Lipschitz continuity of a vector-valued function as presented in a lemma from a textbook on Ordinary Differential Equations. Participants explore the implications of the lemma, the conditions under which it holds, and the mathematical reasoning behind the proof, particularly focusing on the continuity and boundedness of derivatives in a specified domain.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant questions the derivation of an inequality in the proof, specifically how the expression in equation (3) is obtained from earlier steps, suggesting a potential error in their understanding.
  • Another participant provides an upper bound for an integral, proposing a method to relate the supremum of the function to the differences in vector components, leading to a bound that aligns with the lemma's conclusion.
  • A third participant inquires about the necessity of the function being continuously differentiable (C^1) in the closure of the domain, seeking clarification on the implications of this requirement.
  • A later reply explains that continuity of derivatives on a compact domain ensures boundedness, referencing the generalization of a theorem regarding continuous functions on closed intervals.

Areas of Agreement / Disagreement

Participants express differing views on the derivation of the Lipschitz condition and the necessity of the C^1 requirement in the lemma. The discussion remains unresolved regarding the specific derivation questioned, while there is some agreement on the importance of continuity for boundedness.

Contextual Notes

The discussion highlights the dependence on the definitions of Lipschitz continuity and the conditions of the domain, particularly regarding the closure and boundedness of the set involved. There is also an acknowledgment of the potential for misunderstanding in the mathematical reasoning presented.

psie
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TL;DR
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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A basic upper bound for the value of an integral of F: [a,b] \to \mathbb{R} is <br /> \int_a^b F(s)\,ds \leq (b- a)\sup_{s \in [a,b]} F(s). By extension, given I = \int_0^1 \sum_{i=1}^n\|\mathbf{F}_i(t,s)\||x_i - y_i|\,ds we can obtain \begin{split}<br /> I &amp;\leq \sum_{i=1}^n \sup_s \|\mathbf{F}_i\||x_i - y_i| \\<br /> &amp;\leq \left(\max_i \sup_s \|\mathbf{F}_i\|\right) \sum_{i=1}^n |x_i - y_i|.\end{split} Now <br /> \begin{split}<br /> \sum_{i=1}^n |x_i - y_i| &amp;= \left|(1, \dots, 1) \cdot (|x_1-y_1|, \dots, |x_n - y_n|)\right| \\<br /> &amp; \leq \| (1, \dots, 1) \| \|(|x_1-y_1|, \dots, |x_n - y_n|)\| \\<br /> &amp; = \sqrt{n} \|\mathbf{x} - \mathbf{y}\| \\<br /> &amp; \leq n \|\mathbf{x} - \mathbf{y}\|.\end{split} so that <br /> 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}\|.
 
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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##.
 
To guarantee boundedness of the derivatives we require that they be continuous (hence C^1) 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. x^{-1} on (0,1]).

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