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\documentclass{article}
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\input{~/projects/latex/dist/recommended.tex}
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\setup{Summaries}
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\begin{document}
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\startDocumentNoTitle
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\usetcolorboxes
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% TODO: Create and add logo
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\vspace{2cm}
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\begin{center}
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\begin{Huge}
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This summary and more are available at \hlurl{https://github.com/janishutz/summaries}
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\end{Huge}
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\end{center}
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\begin{large}
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This allows for quicker updating in case errors are found and you will always have access to the latest version
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\end{large}
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\end{document}
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\documentclass{article}
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\newcommand{\dir}{~/projects/latex}
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\input{\dir/include.tex}
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\load{recommended}
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\input{~/projects/latex/dist/recommended.tex}
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\setupCheatSheet{Analysis II}
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@@ -59,13 +57,16 @@ If you discover any errors, please open an issue or fix the issue yourself and t
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This Cheat-Sheet was designed with the HS2025 page limit of 10 A4 pages in mind.
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Thus, the whole Cheat-Sheet can be printed full-sized, if you exclude the title page, contents and this page.
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You could also print it as two A5 pages per A4 page and also print the
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\color{MidnightBlue}\fbox{\href{https://github.com/janishutz/eth-summaries/blob/master/semester2/analysis-i/cheat-sheet.pdf}{Analysis I summary}}\color{black}
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\hlhref{https://github.com/janishutz/eth-summaries/blob/master/semester2/analysis-i/cheat-sheet-jh/cheat-sheet-en.pdf}{Analysis I summary}
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\smallhspace in the same manner, allowing you to bring both to the exam.
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And yes, she did really miss an opportunity there with the quote\dots But she was also sick, so it's not as unexpected
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This summary also uses tips and tricks from this \hlhref{https://polybox.ethz.ch/index.php/s/WBGFTRdEjRwJjQC}{Exercise Session}
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% TODO: Everywhere: Check with TA notes to add tips and tricks
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% ╭────────────────────────────────────────────────╮
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% │ Content │
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% ╰────────────────────────────────────────────────╯
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@@ -76,10 +77,23 @@ And yes, she did really miss an opportunity there with the quote\dots But she wa
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\input{parts/diffeq/linear-ode/01_order-one.tex}
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\input{parts/diffeq/linear-ode/02_constant-coefficient.tex}
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\newsection
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\section{Differential Calculus in Vector Space}
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\input{parts/vectors/differentiation/00_continuity.tex}
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\input{parts/vectors/differentiation/01_partial_derivatives.tex}
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\input{parts/vectors/differentiation/02_differential.tex}
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\input{parts/vectors/differentiation/03_higher_diff.tex}
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\input{parts/vectors/differentiation/04_change_of_variable.tex}
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\input{parts/vectors/differentiation/05_taylor_polynomials.tex}
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\input{parts/vectors/differentiation/06_critical_points.tex}
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\newsection
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\section{Integral Calculus in Vector Space}
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\input{parts/vectors/integration/00_line_integrals.tex}
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\input{parts/vectors/integration/01_int_in_rn.tex}
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\input{parts/vectors/integration/02_improper_int.tex}
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\input{parts/vectors/integration/03_change_of_variable_formula.tex}
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\input{parts/vectors/integration/04_green_formula.tex}
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\end{document}
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@@ -66,3 +66,4 @@ Furthermore: $\{ x\in \R^3 : ||x - x_0|| = r \}$ is closed\\
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\shorttheorem Let $(X \neq \emptyset) \subseteq \R^n$ compact and $f: X \rightarrow \R$ continuous.
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Then $f$ bounded, has $\max$ and $\min$, i.e. $\exists x_+, x_- \in X$ s.t. $\displaystyle f(x_+) = \sup_{x \in X} f(x)$ and $\displaystyle f(x_-) = \inf_{x \in X} f(x)$
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% ────────────────────────────────────────────────────────────────────
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\rmvspace
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@@ -8,17 +8,36 @@ $\{ y = (y_1, \ldots, y_n) \in \R^n : |x_i - y_i| < \delta \smallhspace \forall
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\shortex \bi{(1)} $\emptyset$ and $\R^n$ are both open and closed.
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\bi{(2)} Open ball $D = \{ x \in \R^n : ||x - x_0|| < r \}$ is open in $\R^n$ ($x_0$ the center and $r$ radius)
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\bi{(3)} $I_1 \times \dots \times I_n$ is open in $\R^n$ for $I_i$ open
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\bi{(4)} $X \subseteq \R^n$ open $\Leftrightarrow$ $\forall x \in X \exists \delta > 0$ s.t. open ball of center $x$ and radius $\delta$ is contained in $X$
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\bi{(4)} $X \subseteq \R^n$ open $\Leftrightarrow$ $\forall x \in X \exists \delta > 0$ s.t. open ball of center $x$ and radius $\delta$ is contained in $X$\\
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% ────────────────────────────────────────────────────────────────────
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\compactdef{Partial derivative} Let $X \subseteq \R^n$ open, $f: X \rightarrow \R^m$ and $1 \leq i \leq n$.
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Then $f$ has partial derivative on $X$ with respect to the $i$-th variable (or coordinate),
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if $\forall x_0 = (x_{0, 1}, \ldots, x_{0, n}) \in X$, $g(t) = f(x_{0, 1}, \ldots, x_{0, i - 1}, t, x_{0, i + 1}, x_{0, n})$ on set
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$I = \{ t \in \R : (x_{0, 1}, \ldots, x_{0, i - 1}, t, x_{0, i + 1}, \ldots, x_{0, n}) \in X \}$ is differentiable at $t = x_{0, i}$.
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The derivative $g'(x_{0, i})$ at $x_{0, i}$ is denoted:
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$\frac{\partial f}{\partial x_i}(x_0), \partial_{x_i} f(x_0) \text{ or } \partial_i f(x_0)$\\
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%
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% ────────────────────────────────────────────────────────────────────
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\stepLabelNumber{all}
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\shortproposition Let $X \subseteq \R^n$ open, $f, g : X \rightarrow \R^m$ and $1 \leq i \leq n$. Then:
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\bi{(1)} If $f$ \& $g$ have $\partial_i$ on $X$, then so does $f + g$ and $\partial_{x_i} (f + g) = \partial_{x_i}(f) + \partial_{x_i}(g)$
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\bi{(2)} If $m = 1$ (i.e. $\R^1$) and $f$ \& $g$ have $\partial_i$ on $X$, then so does $fg$ and $\partial_{x_i} (fg) = \partial_{x_i}(f)g + f \partial_{x_i}(g)$
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and if $g(x) \neq 0 \smallhspace \forall x \in X$, then if $f \div g$ has $\partial_i$ on $X$, then so does $f \div g$ and
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$\partial_{x_i}(f \div g) = (\partial_{x_i}(f) g - f \partial_{x_i}(g)) \div g^2$
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$\partial_{x_i}(f \div g) = (\partial_{x_i}(f) g - f \partial_{x_i}(g)) \div g^2$\\
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% ────────────────────────────────────────────────────────────────────
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\compactdef{Jacobi Matrix $J$} Element $J_ij = \partial_{x_j} f_i(x)$ for function $f: X \rightarrow \R^m$ with $X \subseteq \R^n$ open. $x_j$ is the $j$-th variable,
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$f_i$ is the $i$-th component of the equation (i.e. in the vector of the function). $J$ has $m$ rows and $n$ columns.\\
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% ────────────────────────────────────────────────────────────────────
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\drmvspace\drmvspace
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\stepLabelNumber{all}
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\compactdef{Gradient, Divergence} for $f : X \rightarrow \R$ with $X \in \R^n$ open, the \bi{gradient} is given by
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$\nabla f(x_0) = \begin{pmatrix}
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\partial_{x_1} f(x_0) \\
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\vdots \\
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\partial_{x_n} f(x_0)
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\end{pmatrix}$
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and the
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\drmvspace\rmvspace
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trace of the Jacobi Matrix, $\text{div}(f)(x_0) = \text{Tr}(J_f(x_0)) = \sum_{i = 1}^{n} \partial_{x_i} f_i(x_0)$ is called the \bi{divergence} of $f$ at $x_0$.
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\rmvspace
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\newsection
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\subsection{The differential}
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\setLabelNumber{all}{2}
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\compactdef{Differentiable function} We have function $f: X \rightarrow \R^m$, linear map $u : \R^n \rightarrow \R^m$ and $x_0 \in X$. $f$ is differentiable at $x_0$ with differential $u$ if
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$\displaystyle \lim_{\elementstack{x \rightarrow x_0}{x \neq x_0}} \frac{1}{||x - x_0||} (f(x) - f(x_0) - u(x - x_0) = 0$ where the limit is in $\R^m$.
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We denote $\dx f(x_0) = u$.
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If $f$ is differentiable at every $x_0 \in X$, then $f$ is differentiable on $X$.
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% ────────────────────────────────────────────────────────────────────
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\stepLabelNumber{all}
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\shortproposition
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\rmvspace Let $f: X \rightarrow \R^m$ be differentiable on $X$
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\begin{itemize}[noitemsep]
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\item $f$ is continuous on $X$
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\item $f$ admits partial derivatives on $X$ with respect to each variable
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\item Assume $m = 1$, let $x_0 \in X$ and let $u(x_1, \ldots, x_n) = a_1 x_1 + \ldots + a_n x_n$ be diff. of $f$ at $x_0$.
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Then $\partial_{x_i} f(x_0) = a_i$ for $1 \leq i \leq n$
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\end{itemize}
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\rmvspace
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% ────────────────────────────────────────────────────────────────────
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\stepLabelNumber{all}
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\shortproposition Let $f, g : X \rightarrow \R^m$ with $X \subseteq \R^n$ open
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\rmvspace
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\begin{itemize}[noitemsep]
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\item The function $f + g$ is differentiable with differential $\dx (f + g) = \dx f + \dx g$. If $m = 1$, then $fg$ is differentiable
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\item If $m = 1$ and if $g(x) \neq 0 \forall x \in X$, then $f \div g$ is differentiable
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\end{itemize}
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\rmvspace
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% ────────────────────────────────────────────────────────────────────
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\shortproposition If $f$ as above has all partial derivatives on $X$ and if they are all continuous on $X$, then $f$ is differentiable on $X$.
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The differential is the Jacobi Matrix of $f$ at $x_0$.
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This implies that most elementary functions are differentiable.\\
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% ────────────────────────────────────────────────────────────────────
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\compactproposition{Chain Rule} For $X \subseteq \R^n$ and $Y \subseteq \R^m$ both open and $f: X \rightarrow Y$ and $g : Y \rightarrow \R^p$ are both differentiable.
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Then $g \circ f$ is differentiable on $X$ and for any $x \in X$, its differential is given by
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$\dx (g \circ f)(x_0) = \dx g(f(x_0)) \circ \dx f(x_0)$.
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The Jacobi matrix is $J_{g \circ f}(x_0) = J_g(f(x_0)) J_f(x_0)$ (RHS is a matrix product)\\
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% ────────────────────────────────────────────────────────────────────
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\setLabelNumber{all}{11}
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\compactdef{Tangent space} The graph of the affine linear approximation $g(x) = f(x_0) + u(x - x_0)$, or the set
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\vspace{-0.75pc}
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\begin{align*}
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\{ (x, y) \in \R^n \times \R^m : y = f(x_0) + u(x - x_0) \}
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\end{align*}
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\drmvspace\rmvspace
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% ────────────────────────────────────────────────────────────────────
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\stepLabelNumber{all}
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\compactdef{Directional derivative} $f$ has a directional derivative $w \in \R^m$ in the direction of $v \in \R^n$,
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if the function $g$ defined on the set $I = \{ t \in \R : x_0 + tv \in X \}$ by $g(t) = f(x_0 + tv)$ has a derivative at $t = 0$ and is equal to $w$
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% ────────────────────────────────────────────────────────────────────
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\shortremark Because $X$ is open, the set $I$ contains an open interval $]-\delta, \delta[$ for some $\delta > 0$.
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% ────────────────────────────────────────────────────────────────────
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\shortproposition Let $f$ as previously be differentiable. Then for any $x \in X$ and non-zero $v \in \R^n$,
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$f$ has a directional derivative at $x_0$ in the direction of $v$, given by$\dx f(x_0)(v)$
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% ────────────────────────────────────────────────────────────────────
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\shortremark The values of the above directional derivative are linear with respect to the vector $v$.
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Suppose we know the dir. der. $w_1$ and $w_2$ in directions $v_1$ and $v_2$, then the directional derivative in direction $v_1 + v_2$ is $w_1 + w_2$
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\newsectionNoPB
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\subsection{Higher derivatives}
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\newsectionNoPB
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\subsection{Change of variable}
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\newsectionNoPB
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\subsection{Taylor polynomials}
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\newsectionNoPB
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\subsection{Critical points}
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\newsectionNoPB
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\subsection{Line integrals}
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\newsectionNoPB
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\subsection{Riemann integral in Vector Space}
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\newsectionNoPB
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\subsection{Improper integrals}
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\newsectionNoPB
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\subsection{Change of Variable Formula}
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\newsectionNoPB
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\subsection{The Green Formula}
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