added multivar calc
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\section{Partial and Total Differentiability}
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\begin{defi}
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Let $U \subset \realn^n$ be open, $x \in (x_1, \cdots, x_n) \in U$ and define the function $f: U \rightarrow \realn^m$.
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The mapping $f$ is said to be partially differentiable in $x$ in terms of $x_i$ if
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\[
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t \longmapsto f(x_1, \cdots, x_{i-1}, t, x_{i+1}, \cdots, x_n)
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\]
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is differentiable in $x_i$, i.e.
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\[
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\partial_i f(x) = \limes{h}{0} \frac{f(x_1, \cdots, x_{i-1}, x_i + h, x_{i+1}, \cdots, x_n) - f(x_1, \cdots, x_n)}{h}
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\]
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exists. $\partial_i f(x)$ is said to be the partial derivative of $f$ in $x$ in terms of $x_i$. Another notation is
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\[
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\pdv{f}{x_i}
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\]
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This mapping is said to be partially differentiable in $x$ if it is partially differentiable in terms of $x_i ~~\forall i \in \set{1, \cdots, n}$.
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\end{defi}
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\begin{eg}
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Consider
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\begin{align*}
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f: \realn^2 &\longrightarrow \realn \\
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(x, y) &\longmapsto \begin{cases}
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1, & x = 0 \vee y = 0 \\
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0, & \text{else}
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\end{cases}
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\end{align*}
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$f$ is partially differentiable in $(0, 0)$, but not continuous.
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\end{eg}
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\begin{thm}
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Let $U \subset \realn$ be open, $x \in U$ and $f: U \rightarrow \field$.
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\begin{gather*}
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f \text{ is differentiable in } x \\
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\iff \\
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\exists a \in \field, \phi: U \rightarrow \field: ~~f(y) = f(x) + a(y - x) + \phi(y) ~~\forall y \in U
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\end{gather*}
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and
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\[
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\limes{y}{x} \frac{\phi(x)}{\abs{y - x}} = 0
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\]
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\end{thm}
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\begin{proof}
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We will first prove the "$\impliedby$" direction. So let $a, \phi$ be as demanded in the theorem. Then
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\begin{equation}
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\frac{f(y) - f(x)}{y - x} = a + \frac{\phi(y)}{\abs{y - x}} \cdot \frac{\abs{y - x}}{y - x} \conv{y \rightarrow x} a
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\end{equation}
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which means $f$ is differentiable in $x$ and $f'(x) = a$. Now let $f$ be differentiable, and set
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\begin{equation}
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\phi(y) = f(y) - f(x) - f'(x)(y - x)
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\end{equation}
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Which is equivalent to the equation in the theorem, with $a = f'(x)$. Then
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\begin{equation}
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\limes{y}{x} \frac{\phi(x)}{\abs{y - x}} = \left( \frac{f(y) - f(x)}{y - x} - f'(x) \right) \cdot \frac{y - x}{\abs{y - x}} = 0
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\end{equation}
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\end{proof}
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\begin{defi}
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Let $U \subset \realn^n$, $x \in U$ and $f: U \rightarrow \realn^m$.
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$f$ is said to be (totally) differentiable in $x$ if a matrix $A \in \realn^{m \times n}$ and a mapping $\phi: U \rightarrow \realn^m$ exist, such that
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\[
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f(y) = f(x) + A(y - x) + \phi(x) ~~\forall y \in U
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\]
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and
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\[
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\limes{y}{x} \frac{\phi(y)}{\norm{y - x}} = 0
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\]
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$f$ is said to be (totally) differentiable if it is (totally) differentiable in every point $x \in U$.
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\end{defi}
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\begin{thm}
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Let $U \subset \realn^n$ be open, $x \in U$ and $f: U \rightarrow \realn^m$ with
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\[
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f = (f_1, \cdots, f_m), ~~f_1, \cdots, f_m: U \longrightarrow \realn
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\]
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If $f$ is totally differentiable in $x$, then it is partially differentiable as well, and the matrix $A$ is given by
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\[
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a_{ji} = \partial_i f_j(x)
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\]
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\end{thm}
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\begin{proof}
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Let $A, \phi$ be as demanded above. Let $e_1, \cdots, e_n$ be the canonical basis for $\realn^n$.
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We insert $y = x + he_i$ and receive
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\begin{equation}
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f(x + he_i) = f(x) + h \cdot Ae_i + \phi(x + he_i)
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\end{equation}
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By rearranging this yields
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\begin{equation}
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\frac{f(x + he_i) - f(x)}{h} = Ae_i + \frac{\phi(x + he_i)}{|h|} \cdot \frac{|h|}{h} \conv{h \rightarrow 0} Ae_i
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\end{equation}
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Thus, $f$ is partially differentiable in $x$ in terms of $x_i$ with $\partial_i f(x) = Ae_i$.
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\end{proof}
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\begin{defi}
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The matrix $(\partial_i f_j(x))_{ij}$ is called the Jacobian matrix of $f$ in $x$.
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We write $Df(x)$. If $f$ is totally differentiable, then $Df(x)$ is said to be the (total) derivative of $f$ in $x$.
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For $m = 1$ (so $f: \realn^n \rightarrow \realn$), the Jacobian matrix has one column, and we call it gradient
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\[
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Df(x) =: \grad f(x)
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\]
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Note: I will adhere to the physical notation of the gradient, using the Nabla operator $\nabla$.
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\end{defi}
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\begin{eg}
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Let $A \in \realn^{m \times n}$ and define
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\begin{align*}
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f_A: \realn^n &\longrightarrow \realn^m \\
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x &\longmapsto Ax
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\end{align*}
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Then we have
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\[
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f_A(y) = Ay = Ax + A(y - x) = f_A(x) - f_A(y - x)
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\]
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Thus, $f_A$ is differentiable $(\phi = 0)$ and the derivative is
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\[
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Df_A(x) = A ~~\forall x \in \realn^n
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\]
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For another example, let
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\begin{align*}
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f: (0, \infty) \times (0, 2\pi) &\longrightarrow \realn^2 \\
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(r, \phi) &\longmapsto (r \cos \phi, r \sin \phi)
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\end{align*}
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Then $f$ is partially differentiable.
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\[
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Df(r, \phi) = \begin{pmatrix}
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\cos\phi & -r\sin\phi \\
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\sin\phi & r\cos\phi
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\end{pmatrix}
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\]
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So $f$ is also totally differentiable (We'll get back to this later).
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\end{eg}
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\begin{rem}
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\begin{enumerate}[(i)]
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\item Let $U \subset \realn^n$ be open and $f: U \rightarrow \realn^m$ differentiable,
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then the derivative $Df$ is a function $U \rightarrow \realn^{m \times n}$
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\item Total differentiability is also called local linear approximation. Linearity is the property
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\[
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A(x + \lambda y) = Ax + \lambda Ay ~~\forall x, y \in \realn^n ~\lambda \in \realn
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\]
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\item For arbitrary vector spaces $V, W$, a mapping $V \rightarrow W$ is said to be linear if
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\[
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A(x + \lambda y) = Ax + \lambda Ay ~~\forall x, y \in \realn^n ~\lambda \in \realn
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\]
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So we can analogously define differentiability for mappings $f: V \rightarrow W$ between arbitrary normed vector spaces.
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\item $f$ is totally differentiable in $x$ if and only if the Jacobian matrix exists and
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\[
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\limes{x}{y} \frac{f(y) - f(x) - Df(x)(y-x)}{\norm{y-x}} = 0
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\]
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\item Let $f = (f_1, \cdots, f_m)$ with $f_1, \cdots, f_m: U \rightarrow \realn$.
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\[
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f \text{ totally differentiable} \iff f_i \text{ totally differentiable } ~\forall i \in \set{1, \cdots, n}
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\]
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The Jacobian matrix $Df_i(x)$ is the $i$-th row of $Df(x)$.
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\item Total differentiability implies continuity.
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\item Partial and total differentiability are local properties.
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\item The mapping $h \mapsto Df(x) \cdot h$ is linear.
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\item The derivative $x \mapsto Df(x)$ is not linear in general.
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\end{enumerate}
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\end{rem}
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\begin{thm}[Chain rule]
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Let $U \subset \realn^n$ be open, $V \subset \realn^m$ open, $x \in U$, $g: U \rightarrow V$ differentiable in $x$, and $f: V \rightarrow \realn^k$ differentiable in $g(x)$.
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Then $f \circ g$ is differentiable and
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\[
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D(f \circ g) = Df(g(x)) \cdot Dg(x)
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\]
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\end{thm}
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\begin{proof}
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Differentiability of $g$ in $x$ means
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\begin{equation}
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\exists \phi_g: U \longrightarrow \realn^m: ~~g(y) - g(x) = D_g(x)(y-x) + \phi_g(y)
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\end{equation}
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Differentiability of $f$ in $g(x)$ means
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\begin{equation}
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\exists \phi_f: V \rightarrow \realn^k:: ~\limes{z}{g(x)} \phi_f(z) \inv{\norm{z - g(x)}} = 0
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\end{equation}
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and
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\begin{equation}
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f(z) = f(g(x)) + D_f(g(x))(z - g(x)) + \phi_f(z)
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\end{equation}
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Now set $z = g(y)$, then
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\begin{equation}
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\begin{split}
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\underbrace{f(g(y))}_{(f \circ g)(y)} = \underbrace{f(g(x))}_{(f \circ g)(x)} &+ D_f(g(x)) \cdot D_g(x)(y-x) \\
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&+ (D_f(g(x)) \phi_g(y) + \phi_f(g(y)))
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\end{split}
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\end{equation}
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And we finally need to show
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\begin{equation}
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\frac{D_f(g(x)) \phi_g(y) + \phi_f(g(y))}{\norm{y - x}} \conv{y \rightarrow x} 0
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\end{equation}
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We know that
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\begin{equation}
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Df(g(x)) \frac{\phi_g(y)}{\norm{y - x}} \conv{} 0
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\end{equation}
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because
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\begin{equation}
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z \longmapsto Df(g(x)) z \text{ linear and thus continuous}
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\end{equation}
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We define a new mapping
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\begin{equation}
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\begin{split}
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\psi: U &\longrightarrow \realn \\
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z &\longmapsto \begin{cases}
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\phi_f(z) - \inv{\norm{z - g(x)}}, & z \ne g(x) \\
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0, & z = g(x)
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\end{cases}
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\end{split}
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\end{equation}
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$\psi$ is continuous in $g(x)$. Then $\forall y \in U$ we have
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\begin{equation}
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\frac{\phi_f(g(y))}{\norm{y - x}} = \underbrace{\psi(g(y))}_{\conv{y \rightarrow x} 0} \cdot \frac{\norm{g(y) - g(x)}}{\norm{y - x}}
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\end{equation}
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and
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\begin{equation}
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\begin{split}
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\frac{\norm{g(y) - g(x)}}{\norm{y - x}} &= \norm{Dg(x) \frac{y - x}{\norm{y - x}} + \frac{\phi_g(y)}{\norm{y - x}}} \\
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&\le \underbrace{\norm{Dg(x) \frac{y - x}{\norm{y - x}}}}_{\le \norm{Dg(x)}} + \underbrace{\norm{\frac{\phi_g(y)}{\norm{y - x}}}}_{\conv{y \rightarrow x} 0}
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\end{split}
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\end{equation}
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thus $\psi$ is bounded.
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\begin{equation}
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\implies \psi(g(y)) \cdot \frac{\norm{g(y) - g(x)}}{\norm{y - x}} \conv{} 0
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\end{equation}
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\end{proof}
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\begin{thm}
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Let $U \subset \realn^n$ and $f: U \longrightarrow \realn^m$. If $\forall x \in U$ the partial derivatives $\partial_i f(x)$ exist and are continuous $\forall i \in \set{1, \cdots, n}$.
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then $f$ is totally differentiable.
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\end{thm}
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\begin{proof}
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Without proof.
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\end{proof}
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\begin{defi}
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Let $U \subset \realn^n$ be open. $f: U \rightarrow \realn^m$ is said to be continuously differentiable if all partial derivatives exist and are continuous.
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The vector space of all such functions is denoted as $C^1(U, \realn^m)$, or in the special case $m=1$ as $C^1(U)$.
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\end{defi}
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\begin{eg}
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\begin{enumerate}
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\item Coming back to a previous example, we consider
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\[
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Df(r, \phi) = \begin{pmatrix}
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\cos \phi & -r \sin \phi \\
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\sin \phi & \cos \phi
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\end{pmatrix}
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\]
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Thus, $f$ is continuously differentiable, and therefore totally differentiable.
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\item Let $N \in \natn$ and $c_{\eta} \in \field$ for every multiindex $\eta \in \natn_0^n$ with $\abs{\eta} \le N$.
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Then the polynomial
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\begin{align*}
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P: \realn^n &\longrightarrow \field \\
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x &\longmapsto \sum_{\substack{\eta \\ \abs{\eta} \le N}} c_{\eta} x^{\eta}
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\end{align*}
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is continuously differentiable, and therefore totally differentiable.
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\begin{align*}
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\partial_i x^{\eta} &= \partial_i \left( x_1^{\eta_1}, x_2^{\eta_2}, \cdots, x_n^{\eta_n} \right) \\
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&= \eta_i x_1^{\eta_1} \cdots x_{i-1}^{\eta_{i-1}} x_i^{\eta_{i-1}} x_{i+1}^{\eta_{i+1}} \cdots x_n^{\eta_n}
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\end{align*}
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This is another polynomial, and therefore continuous.
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\end{enumerate}
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\end{eg}
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We introduce the following new notation, for $x, y \in \realn^n$:
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\begin{align*}
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\oline &:= \set[t \in (0, 1)]{x + t(y - x)} \\
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\cline &:= \set[{t \in [0, 1]}]{x + t(y - x)}
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\end{align*}
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They denote the connecting line between $x$ and $y$.
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\begin{center}
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\begin{tikzpicture}
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\draw (0, 0) circle [radius=2pt] node[above left] {$x$};
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\draw (3, 1.5) circle [radius=2pt] node[above right] {$y$};
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\draw (0, 0) -- node[below right] {$\oline$} (3, 1.5);
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\end{tikzpicture}
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\end{center}
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\begin{thm}[Intermediate value theorem for $\realn$-valued functions]
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Let $U \subset \realn^n$ be oppen, $x, y \in U$ and $\cline \subset U$.
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Now let $f: U \rightarrow \realn$ differentiable on $\oline$ and continuous in $x, y$. Then
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\[
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\exists \xi \in \cline: ~~f(y) - f(x) = Df(\xi) (y-x)
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\]
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\end{thm}
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\begin{proof}
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Consider
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\begin{equation}
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\begin{split}
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g: [0, 1] &\longrightarrow \realn \\
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t &\longmapsto f(x + t(y - x))
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\end{split}
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\end{equation}
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Apply the one dimensional intermediate value theorem. Due to the chain rule, $g$ fulfils the prerequisites.
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$\exists \theta \in (0, 1)$ such that
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\begin{equation}
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f(y) - f(x) = g(1) - g(0) = g(\theta) = Df(x + \theta(y - x))(y - x)
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\end{equation}
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For $\xi = x + \theta(y - x)$ follows the initial statement.
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\end{proof}
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\begin{thm}[Intermediate value theorem]
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Let $U \subset \realn^n$ be open, $\cline \subset U$ and $f: U \rightarrow \realn^m$ differentiable on $\oline$ and continuous in $x, y$. Then
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\[
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\exists \xi \in \oline: ~~\norm{f(y) - f(x)} \le \norm{Df(\xi)(y - x)}
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\]
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\end{thm}
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\begin{proof}
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For $a \in \realn^m$, consider the (real) helper function
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\begin{equation}
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a^Tf(x) = \innerproduct{a}{f(x)}
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\end{equation}
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According to the previous theorem
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\begin{equation}
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\exists \xi \in \oball: ~~a^T f(y) - a^T f(x) = a^T D f(\xi)(y - x)
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\end{equation}
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In this implication the chain rule has been applied. We can rewrite this using the scalar product
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\begin{equation}
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\begin{split}
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\norm{f(y) - f(x)}^2 &= \abs{\innerproduct{f(y) - f(x)}{Df(\xi)(y-x)}} \\
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&\le \norm{f(y) - f(x)}\norm{Df(\xi)(y - x)}
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\end{split}
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\end{equation}
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\end{proof}
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\begin{cor}
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Let $U \subset \realn^n$ be open and $f: U \rightarrow \realn^m$ a differentiable function.
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\[
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Df = 0 \text{ on } U \implies \exists V \subset U: f \text{ constant on } V
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\]
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\end{cor}
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\begin{proof}
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Let $x \in U$, choose $\epsilon > 0$ such that $\oball(x) \subset U$. Then
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\begin{equation}
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\forall y \in \oball(x) ~\exists \xi \in \oline: ~~\norm{f(y) - f(x)} \le \norm{Df(\xi)(y - x)} = 0
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\end{equation}
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This implies
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\begin{equation}
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\norm{f(y) - f(x)} = 0 \implies f(y) = f(x) ~~\forall y \in \oball(x)
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\end{equation}
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\end{proof}
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\begin{rem}
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Functions with vanishing derivatives must be constant. Consider
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\begin{align*}
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f: (-2, -1) \cup (1, 2) &\longrightarrow \\
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x &\longmapsto \begin{cases}
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-1, & x < 0 \\
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1, & x > 0
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\end{cases}
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\end{align*}
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Local constancy implies constancy on connected sets.
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\end{rem}
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\end{document}
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@ -71,6 +71,9 @@
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\newcommand{\Oball}{\oball[r]}
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\newcommand{\Cball}{\cball[r]}
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\newcommand{\oline}[1][{x,y}]{S_{#1}}
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\renewcommand{\cline}[1][{x,y}]{\overline{S_{#1}}}
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\newcommand{\interior}[1]{\mathring{#1}}
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\newcommand{\boundary}[1]{\partial #1}
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\newcommand{\closure}[1]{\bar{#1}}
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Reference in a new issue