diff --git a/semester3/analysis-ii/cheat-sheet-jh/analysis-ii-cheat-sheet.pdf b/semester3/analysis-ii/cheat-sheet-jh/analysis-ii-cheat-sheet.pdf index 0733df9..aafda9c 100644 Binary files a/semester3/analysis-ii/cheat-sheet-jh/analysis-ii-cheat-sheet.pdf and b/semester3/analysis-ii/cheat-sheet-jh/analysis-ii-cheat-sheet.pdf differ diff --git a/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/02_differential.tex b/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/02_differential.tex index 50b9462..e9aa44c 100644 --- a/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/02_differential.tex +++ b/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/02_differential.tex @@ -1,10 +1,12 @@ -\newsection +\newsectionNoPB \subsection{The differential} \setLabelNumber{all}{2} \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 -$\displaystyle \lim_{\elementstack{x \rightarrow x_0}{x \neq x_0}} \frac{1}{||x - x_0||} (f(x) - f(x_0) - u(x - x_0) = 0$ where the limit is in $\R^m$. +$\displaystyle \lim_{\elementstack{x \rightarrow x_0}{x \neq x_0}} \frac{f(x) - f(x_0) - u(x - x_0)}{||x - x_0||} = 0$ where the limit is in $\R^m$. We denote $\dx f(x_0) = u$. If $f$ is differentiable at every $x_0 \in X$, then $f$ is differentiable on $X$. + +\newpage % ──────────────────────────────────────────────────────────────────── \stepLabelNumber{all} \shortproposition @@ -27,13 +29,13 @@ If $f$ is differentiable at every $x_0 \in X$, then $f$ is differentiable on $X$ \rmvspace % ──────────────────────────────────────────────────────────────────── \shortproposition If $f$ as above has all partial derivatives on $X$ and if they are all continuous on $X$, then $f$ is differentiable on $X$. -The differential is the Jacobi Matrix of $f$ at $x_0$. +The \bi{differential is the Jacobi Matrix of $f$ at $x_0$}. This implies that most elementary functions are differentiable.\\ % ──────────────────────────────────────────────────────────────────── \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. Then $g \circ f$ is differentiable on $X$ and for any $x \in X$, its differential is given by $\dx (g \circ f)(x_0) = \dx g(f(x_0)) \circ \dx f(x_0)$. -The Jacobi matrix is $J_{g \circ f}(x_0) = J_g(f(x_0)) J_f(x_0)$ (RHS is a matrix product)\\ +The Jacobi matrix is $J_{g \circ f}(x_0) = J_g(f(x_0)) J_f(x_0)$ (RHS is a matrix product, i.e. multiply rows of first with cols of second matrix)\\ % ──────────────────────────────────────────────────────────────────── \setLabelNumber{all}{11} \compactdef{Tangent space} The graph of the affine linear approximation $g(x) = f(x_0) + u(x - x_0)$, or the set @@ -42,7 +44,7 @@ The Jacobi matrix is $J_{g \circ f}(x_0) = J_g(f(x_0)) J_f(x_0)$ (RHS is a matri \{ (x, y) \in \R^n \times \R^m : y = f(x_0) + u(x - x_0) \} \end{align*} -\drmvspace\rmvspace +\dnrmvspace % ──────────────────────────────────────────────────────────────────── \stepLabelNumber{all} \compactdef{Directional derivative} $f$ has a directional derivative $w \in \R^m$ in the direction of $v \in \R^n$, @@ -55,3 +57,29 @@ $f$ has a directional derivative at $x_0$ in the direction of $v$, given by$\dx % ──────────────────────────────────────────────────────────────────── \shortremark The values of the above directional derivative are linear with respect to the vector $v$. 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 $w_1 + w_2$ + +\shade{gray}{Computing a directional derivative} Always normalize the vector! We can compute a directional derivative using the differential $\limit{h}{0} \frac{f(x_0 + hv) - f(x_0)}{h}$ +or using a $1$-dimensional helper function $g: h \mapsto f(x_0 + hv)$, calculating the derivative of it and evaluating $g'(0)$. That corresponds to the directional derivative. +E.g. for function $f: x, y \mapsto x^2 + y^2$, we have $g: h \mapsto (x_0 + h)^2 + (y_0 + h)^2$. +A final option is to compute it using a matrix-vector product: $D_v f(x_0) = J_f(x_0) v$ + +\rmvspace +\begin{center} + \begin{tikzpicture}[node distance = 0.5cm and 0.5cm, >={Classical TikZ Rightarrow[width=7pt]}] + \node (contdiff) {$f$ cont. diff.}; + \node (diff) [below=of contdiff] {$f$ differentiable}; + \node (cont) [below=of diff] {$f$ continuous}; + \node (diffcont) [right=of contdiff] {All $\partial_j f_i$ continuous}; + \node (diffex) [below=of diffcont] {All $\partial_j f_i$ exist}; + \node (notes) at (-5, -1) {Red arrows indicate no implication}; + + \draw[arrows = ->, double distance = 1.5pt] (contdiff) -- (diff); + \draw[arrows = ->, double distance = 1.5pt] (diff) -- (cont); + \draw[arrows = <->, double distance = 1.5pt] (contdiff) -- (diffcont); + \draw[arrows = ->, double distance = 1.5pt] (diffcont) -- (diffex); + \draw[arrows = ->, double distance = 1.5pt, transform canvas={yshift=0.2cm}] (diff) -- (diffex); + \draw[arrows = ->, double distance = 1.5pt, transform canvas={yshift=-0.2cm}, color=red] (diffex) -- (diff); + \draw[arrows = ->, double distance = 1.5pt, color=red] (diffex) -- (cont); + \end{tikzpicture} +\end{center} +\drmvspace diff --git a/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/05_taylor_polynomials.tex b/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/05_taylor_polynomials.tex index 723ea83..58fdcc1 100644 --- a/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/05_taylor_polynomials.tex +++ b/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/05_taylor_polynomials.tex @@ -1,4 +1,4 @@ -\newsectionNoPB +\newsection \subsection{Taylor polynomials} \compactdef{Taylor polynomials} Let $f : X \rightarrow \R$ with $f \in C^k(X, \R)$ and $y \in X$. The Taylor-Polynomial of order $k$ of $f$ at $y$ is: diff --git a/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/06_critical_points.tex b/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/06_critical_points.tex index ca6c209..b4e0ecd 100644 --- a/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/06_critical_points.tex +++ b/semester3/analysis-ii/cheat-sheet-jh/parts/vectors/differentiation/06_critical_points.tex @@ -1,4 +1,4 @@ -\newsection +\newsectionNoPB \subsection{Critical points} \stepLabelNumber{all} \compactdef{Critical Point} For $f: X \rightarrow \R^n$ differentiable, $x_0 \in X$ is called a \bi{critical point} of $f$ if $\nabla f(x_0) = 0$