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LaTeX-examples/documents/math-minimal-distance-to-cubic-function/math-minimal-distance-to-cubic-function.tex
Martin Thoma 1a69ea8475 one step closer in verification step
2013-12-11 18:45:47 +01:00

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\documentclass[a4paper]{scrartcl}
\usepackage{amssymb, amsmath} % needed for math
\usepackage{mathtools} % \xRightarrow
\usepackage[utf8]{inputenc} % this is needed for umlauts
\usepackage[english]{babel} % this is needed for umlauts
\usepackage[T1]{fontenc} % this is needed for correct output of umlauts in pdf
\usepackage[margin=2.5cm]{geometry} %layout
\usepackage{hyperref} % links im text
\usepackage{braket} % needed for \Set
\usepackage{parskip}
\usepackage[colorinlistoftodos]{todonotes}
\usepackage{pgfplots}
\pgfplotsset{compat=1.7,compat/path replacement=1.5.1}
\usepackage{tikz}
\usepackage[framed,amsmath,thmmarks,hyperref]{ntheorem}
\usepackage{framed}
\usepackage{nicefrac}
\usepackage{siunitx}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Define theorems %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\theoremstyle{break}
\setlength\theoremindent{0.7cm}
\theoremheaderfont{\kern-0.7cm\normalfont\bfseries}
\theorembodyfont{\normalfont} % nicht mehr kursiv
\def\mdr{\ensuremath{\mathbb{R}}}
\renewcommand{\qed}{\hfill\blacksquare}
\newframedtheorem{theorem}{Theorem}
\newframedtheorem{lemma}[theorem]{Lemma}
\newtheorem{plaindefinition}{Definition}
\newenvironment{definition}{\begin{plaindefinition}}{\end{plaindefinition}}
\newenvironment{definition*}{\begin{plaindefinition*}}{\end{plaindefinition*}}
\newtheorem{example}{Example}
\theoremstyle{nonumberplain}
\newtheorem{proof}{Proof:}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\title{Minimal distance to a cubic function}
\author{Martin Thoma}
\hypersetup{
pdfauthor = {Martin Thoma},
pdfkeywords = {},
pdftitle = {Minimal Distance}
}
\def\mdr{\ensuremath{\mathbb{R}}}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Begin document %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}
\maketitle
\begin{abstract}
When you want to develop a selfdriving car, you have to plan which path
it should take. A reasonable choice for the representation of
paths are cubic splines. You also have to be able to calculate
how to steer to get or to remain on a path. A way to do this
is applying the \href{https://en.wikipedia.org/wiki/PID_algorithm}{PID algorithm}.
This algorithm needs to know the signed current error. So you need to
be able to get the minimal distance of a point to a cubic spline combined with the direction (left or right).
As you need to get the signed error (and one steering direction might
be prefered), it is not only necessary to
get the minimal absolute distance, but might also help to get all points
on the spline with minimal distance.
In this paper I want to discuss how to find all points on a cubic
function with minimal distance to a given point.
As other representations of paths might be easier to understand and
to implement, I will also cover the problem of finding the minimal
distance of a point to a polynomial of degree 0, 1 and 2.
\end{abstract}
\section{Description of the Problem}
Let $f: \mdr \rightarrow \mdr$ be a polynomial function and $P \in \mdr^2$
be a point. Let $d_{P,f}: \mdr \rightarrow \mdr_0^+$
be the Euklidean distance of a point $P$ and a point $\left (x, f(x) \right )$
on the graph of $f$:
\[d_{P,f} (x) := \sqrt{(x_P - x)^2 + (y_P - f(x))^2}\]
Now there is finite set $M = \Set{x_1, \dots, x_n}$ of minima for given $f$ and $P$:
\[M = \Set{x \in \mdr | d_{P,f}(x) = \min_{\overline{x} \in \mdr} d_{P,f}(\overline{x})}\]
But minimizing $d_{P,f}$ is the same as minimizing $d_{P,f}^2$:
\begin{align}
d_{P,f}(x)^2 &= \sqrt{(x_P - x)^2 + (y_P - f(x))^2}^2\\
&= x_p^2 - 2x_p x + x^2 + y_p^2 - 2y_p f(x) + f(x)^2
\end{align}
\begin{theorem}[Fermat's theorem about stationary points]\label{thm:required-extremum-property}
Let $x_0$ be a local extremum of a differentiable function $f: \mathbb{R} \rightarrow \mathbb{R}$.
Then: $f'(x_0) = 0$.
\end{theorem}
\clearpage
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Constant functions %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Minimal distance to a constant function}
Let $f(x) = c$ with $c \in \mdr$ be a constant function.
\begin{figure}[htp]
\centering
\begin{tikzpicture}
\begin{axis}[
legend pos=north west,
axis x line=middle,
axis y line=middle,
grid = major,
width=0.8\linewidth,
height=8cm,
grid style={dashed, gray!30},
xmin=-5, % start the diagram at this x-coordinate
xmax= 5, % end the diagram at this x-coordinate
ymin= 0, % start the diagram at this y-coordinate
ymax= 3, % end the diagram at this y-coordinate
axis background/.style={fill=white},
xlabel=$x$,
ylabel=$y$,
tick align=outside,
minor tick num=-3,
enlargelimits=true,
tension=0.08]
\addplot[domain=-5:5, thick,samples=50, red] {1};
\addplot[domain=-5:5, thick,samples=50, green] {2};
\addplot[domain=-5:5, thick,samples=50, blue, densely dotted] {3};
\addplot[black, mark = *, nodes near coords=$P$,every node near coord/.style={anchor=225}] coordinates {(2, 2)};
\addplot[blue, mark = *, nodes near coords=$P_{h,\text{min}}$,every node near coord/.style={anchor=225}] coordinates {(2, 3)};
\addplot[green, mark = x, nodes near coords=$P_{g,\text{min}}$,every node near coord/.style={anchor=120}] coordinates {(2, 2)};
\addplot[red, mark = *, nodes near coords=$P_{f,\text{min}}$,every node near coord/.style={anchor=225}] coordinates {(2, 1)};
\draw[thick, dashed] (axis cs:2,0) -- (axis cs:2,3);
\addlegendentry{$f(x)=1$}
\addlegendentry{$g(x)=2$}
\addlegendentry{$h(x)=3$}
\end{axis}
\end{tikzpicture}
\caption{Three constant functions and their points with minimal distance}
\label{fig:constant-min-distance}
\end{figure}
Then $(x_P,f(x_P))$ has
minimal distance to $P$. Every other point has higher distance.
See Figure~\ref{fig:constant-min-distance}.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Linear functions %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Minimal distance to a linear function}
Let $f(x) = m \cdot x + t$ with $m \in \mdr \setminus \Set{0}$ and
$t \in \mdr$ be a linear function.
\begin{figure}[htp]
\centering
\begin{tikzpicture}
\begin{axis}[
legend pos=north east,
axis x line=middle,
axis y line=middle,
grid = major,
width=0.8\linewidth,
height=8cm,
grid style={dashed, gray!30},
xmin= 0, % start the diagram at this x-coordinate
xmax= 5, % end the diagram at this x-coordinate
ymin= 0, % start the diagram at this y-coordinate
ymax= 3, % end the diagram at this y-coordinate
axis background/.style={fill=white},
xlabel=$x$,
ylabel=$y$,
tick align=outside,
minor tick num=-3,
enlargelimits=true,
tension=0.08]
\addplot[domain=-5:5, thick,samples=50, red] {0.5*x};
\addplot[domain=-5:5, thick,samples=50, blue] {-2*x+6};
\addplot[black, mark = *, nodes near coords=$P$,every node near coord/.style={anchor=225}] coordinates {(2, 2)};
\addlegendentry{$f(x)=\frac{1}{2}x$}
\addlegendentry{$g(x)=-2x+6$}
\end{axis}
\end{tikzpicture}
\caption{The shortest distance of $P$ to $f$ can be calculated by using the perpendicular}
\label{fig:linear-min-distance}
\end{figure}
Now you can drop a perpendicular $f_\bot$ through $P$ on $f(x)$. The
slope of $f_\bot$ is $- \frac{1}{m}$ and $t_\bot$ can be calculated:\nobreak
\begin{align}
f_\bot(x) &= - \frac{1}{m} \cdot x + t_\bot\\
\Rightarrow y_P &= - \frac{1}{m} \cdot x_P + t_\bot\\
\Leftrightarrow t_\bot &= y_P + \frac{1}{m} \cdot x_P
\end{align}
The point $(x, f(x))$ where the perpendicular $f_\bot$ crosses $f$
is calculated this way:
\begin{align}
f(x) &= f_\bot(x)\\
\Leftrightarrow m \cdot x + t &= - \frac{1}{m} \cdot x + \left(y_P + \frac{1}{m} \cdot x_P \right)\\
\Leftrightarrow \left (m + \frac{1}{m} \right ) \cdot x &= y_P + \frac{1}{m} \cdot x_P - t\\
\Leftrightarrow x &= \frac{m}{m^2+1} \left ( y_P + \frac{1}{m} \cdot x_P - t \right )
\end{align}
There is only one point with minimal distance. See Figure~\ref{fig:linear-min-distance}.
\clearpage
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Quadratic functions %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Minimal distance to a quadratic function}
Let $f(x) = a \cdot x^2 + b \cdot x + c$ with $a \in \mdr \setminus \Set{0}$ and
$b, c \in \mdr$ be a quadratic function.
\begin{figure}[htp]
\centering
\begin{tikzpicture}
\begin{axis}[
legend pos=north west,
axis x line=middle,
axis y line=middle,
grid = major,
width=0.8\linewidth,
height=8cm,
grid style={dashed, gray!30},
xmin=-3, % start the diagram at this x-coordinate
xmax= 3, % end the diagram at this x-coordinate
ymin=-0.25, % start the diagram at this y-coordinate
ymax= 9, % end the diagram at this y-coordinate
axis background/.style={fill=white},
xlabel=$x$,
ylabel=$y$,
tick align=outside,
minor tick num=-3,
enlargelimits=true,
tension=0.08]
\addplot[domain=-3:3, thick,samples=50, red] {0.5*x*x};
\addplot[domain=-3:3, thick,samples=50, green] { x*x};
\addplot[domain=-3:3, thick,samples=50, blue] { x*x + x};
\addplot[domain=-3:3, thick,samples=50, orange,dotted] { x*x + 2*x};
\addplot[domain=-3:3, thick,samples=50, black,dashed] {-x*x + 6};
\addlegendentry{$f_1(x)=\frac{1}{2}x^2$}
\addlegendentry{$f_2(x)=x^2$}
\addlegendentry{$f_3(x)=x^2+x$}
\addlegendentry{$f_4(x)=x^2+2x$}
\addlegendentry{$f_5(x)=-x^2+6$}
\end{axis}
\end{tikzpicture}
\caption{Quadratic functions}
\end{figure}
\subsection{Calculate points with minimal distance}
In this case, $d_{P,f}^2$ is polynomial of degree 4.
We use Theorem~\ref{thm:required-extremum-property}:\nobreak
\begin{align}
0 &\overset{!}{=} (d_{P,f}^2)'\\
&= -2 x_p + 2x -2y_p f'(x) + \left (f(x)^2 \right )'\\
&= -2 x_p + 2x -2y_p f'(x) + 2 f(x) \cdot f'(x) \rlap{\hspace*{3em}(chain rule)}\label{eq:minimizingFirstDerivative}\\
\Leftrightarrow 0 &\overset{!}{=} -x_p + x -y_p f'(x) + f(x) \cdot f'(x) \rlap{\hspace*{3em}(divide by 2)}\label{eq:minimizingFirstDerivative}\\
&= -x_p + x -y_p (2ax+b) + (ax^2+bx+c)(2ax+b)\\
&= -x_p + x -y_p \cdot 2ax- y_p b + (2 a^2 x^3+2 a b x^2+2 a c x+ab x^2+b^2 x+bc)\\
&= -x_p + x -2y_p ax- y_p b + (2a^2 x^3 + 3 ab x^2 + 2acx + b^2 x + bc)\\
&= 2a^2 x^3 + 3 ab x^2 + (1 -2y_p a+ 2ac + b^2)x +(bc-by_p-x_p)\label{eq:quadratic-derivative-eq-0}
\end{align}
This is an algebraic equation of degree 3.
There can be up to 3 solutions in such an equation. Those solutions
can be found with a closed formula.
\todo[inline]{Where are those closed formulas?}
\begin{example}
Let $a = 1, b = 0, c= 1, x_p= 0, y_p = 1$.
So $f(x) = x^2 + 1$ and $P(0, 1)$.
\begin{align}
0 &\stackrel{!}{=} 4 x^3 - 2x\\
&=2x(2x^2 - 1)\\
\Rightarrow x_1 &= 0 \;\;\; x_{2,3} = \pm \frac{1}{\sqrt{2}}
\end{align}
As you can easily verify, only $x_1$ is a minimum of $d_{P,f}$.
\end{example}
\subsection{Number of points with minimal distance}
\begin{theorem}
A point $P$ has either one or two points on the graph of a
quadratic function $f$ that are closest to $P$.
\end{theorem}
In the following, I will do some transformations with $f = f_0$ and
$P = P_0$ .
Moving $f_0$ and $P_0$ simultaneously in $x$ or $y$ direction does
not change the minimum distance. Furthermore, we can find the
points with minimum distance on the moved situation and calculate
the minimum points in the original situation.
First of all, we move $f_0$ and $P_0$ by $\frac{b}{2a}$ in $x$ direction, so
\[f_1(x) = ax^2 - \frac{b^2}{4a} + c \;\;\;\text{ and }\;\;\; P_1 = \left (x_p+\frac{b}{2a},\;\; y_p \right )\]
Because:\footnote{The idea why you subtract $\frac{b}{2a}$ within
$f$ is that when you subtract something from $x$ before applying
$f$ it takes more time ($x$ needs to be bigger) to get to the same
situation. So to move the whole graph by $1$ to the left whe have
to add $+1$.}
\begin{align}
f(x-\nicefrac{b}{2a}) &= a (x-\nicefrac{b}{2a})^2 + b (x-\nicefrac{b}{2a}) + c\\
&= a (x^2 - \nicefrac{b}{a} x + \nicefrac{b^2}{4a^2}) + bx - \nicefrac{b^2}{2a} + c\\
&= ax^2 - bx + \nicefrac{b^2}{4a} + bx - \nicefrac{b^2}{2a} + c\\
&= ax^2 -\nicefrac{b^2}{4a} + c
\end{align}
Then move $f_1$ and $P_1$ by $\frac{b^2}{4a}-c$ in $y$ direction. You get:
\[f_2(x) = ax^2\;\;\;\text{ and }\;\;\; P_2 = \Big (\underbrace{x_P+\frac{b}{2a}}_{=: z},\;\; \underbrace{y_P+\frac{b^2}{4a}-c}_{=: w} \Big )\]
\textbf{Case 1:} As $f_2(x) = ax^2$ is symmetric to the $y$ axis, only points
$P = (0, w)$ could possilby have three minima.
Then compute:
\begin{align}
d_{P,{f_2}}(x) &= \sqrt{(x-0)^2 + (f_2(x)-w)^2}\\
&= \sqrt{x^2 + (ax^2-w)^2}\\
&= \sqrt{x^2 + a^2 x^4-2aw x^2+w^2}\\
&= \sqrt{a^2 x^4 + (1-2aw) x^2 + w^2}\\
&= \sqrt{\left (a^2 x^2 + \frac{1-2 a w}{2} \right )^2 + w^2 - (1-2 a w)^2}\\
&= \sqrt{\left (a^2 x^2 + \nicefrac{1}{2}-a w \right )^2 + \big (w^2 - (1-2 a w)^2 \big)}
\end{align}
The term
\[a^2 x^2 + (\nicefrac{1}{2}-a w)\]
should get as close to $0$ as possilbe when we want to minimize
$d_{P,{f_2}}$. For $w \leq \nicefrac{1}{2a}$ you only have $x = 0$ as a minimum.
For all other points $P = (0, w)$, there are exactly two minima $x_{1,2} = \pm \sqrt{aw - \nicefrac{1}{2}}$.
\textbf{Case 2:} $P = (z, w)$ is not on the symmetry axis, so $z \neq 0$. Then you compute:
\begin{align}
d_{P,{f_2}}(x) &= \sqrt{(x-z)^2 + (f(x)-w)^2}\\
&= \sqrt{(x^2 - 2zx + z^2) + ((ax^2)^2 - 2 awx^2 + w^2)}\\
&= \sqrt{a^2x^4 + (1- 2 aw)x^2 +(- 2z)x + z^2 + w^2}\\
0 &\stackrel{!}{=} \Big(\big(d_{P, {f_2}}(x)\big)^2\Big)' \\
&= 4a^2x^3 + 2(1- 2 aw)x +(- 2z)\\
&= 2 \left (2a^2x^2 + (1- 2 aw) \right )x - 2z\\
\Leftrightarrow 0 &\stackrel{!}{=} (2a^2x^2 + (1- 2 aw)) x - z\\
&= 2 a^2 x^3 + (1- 2 aw) x - z\\
\Leftrightarrow 0 &\stackrel{!}{=} x^3 + \underbrace{\frac{(1- 2 aw)}{2 a^2}}_{=: \alpha} x + \underbrace{\frac{-z}{2 a^2}}_{=: \beta}\\
&= x^3 + \alpha x + \beta\label{eq:simple-cubic-equation-for-quadratic-distance}
\end{align}
The solution of Equation~\ref{eq:simple-cubic-equation-for-quadratic-distance}
is
\[t := \sqrt[3]{\sqrt{3 \cdot (4 \alpha^3 + 27 \beta^2)} -9\beta}\]
\[x = \frac{t}{\sqrt[3]{18}} - \frac{\sqrt[3]{\frac{2}{3}} \alpha }{t}\]
When you insert is in Equation~\ref{eq:simple-cubic-equation-for-quadratic-distance}
you get:
\begin{align}
0 &= \left (\frac{t}{\sqrt[3]{18}} - \frac{\sqrt[3]{\frac{2}{3}} \alpha }{t} \right )^3 + \alpha \left (\frac{t}{\sqrt[3]{18}} - \frac{\sqrt[3]{\frac{2}{3}} \alpha }{t} \right ) + \beta\\
&= (\frac{t}{\sqrt[3]{18}})^3 - 3 (\frac{t}{\sqrt[3]{18}})^2 \frac{\sqrt[3]{\frac{2}{3}} \alpha }{t} + 3 (\frac{t}{\sqrt[3]{18}})(\frac{\sqrt[3]{\frac{2}{3}} \alpha }{t})^2 + (\frac{\sqrt[3]{\frac{2}{3}} \alpha }{t})^3 + \alpha \left (\frac{t}{\sqrt[3]{18}} - \frac{\sqrt[3]{\frac{2}{3}} \alpha }{t} \right ) + \beta\\
&= \frac{t^3}{18} - \frac{3t^2}{\sqrt[3]{18^2}} \frac{\sqrt[3]{\frac{2}{3}} \alpha }{t} + \frac{3t}{\sqrt[3]{18}} \frac{\sqrt[3]{\frac{4}{9}} \alpha^2 }{t^2} + \frac{\frac{2}{3} \alpha^3 }{t^3} + \alpha \left (\frac{t}{\sqrt[3]{18}} - \frac{\sqrt[3]{\frac{2}{3}} \alpha }{t} \right ) + \beta\\
&= \frac{t^3}{18} - \frac{\sqrt[3]{18} t \alpha}{\sqrt[3]{18^2}} + \frac{\sqrt[3]{12} \alpha^2}{\sqrt[3]{18} t} + \frac{\frac{2}{3} \alpha^3 }{t^3} + \alpha \left (\frac{t}{\sqrt[3]{18}} - \frac{\sqrt[3]{\frac{2}{3}} \alpha }{t} \right ) + \beta\\
&= \frac{t^3}{18} - \frac{t \alpha}{\sqrt[3]{18}} + \frac{\sqrt[3]{2} \alpha^2}{\sqrt[3]{3} t} + \frac{\frac{2}{3} \alpha^3 }{t^3} + \alpha \left (\frac{t}{\sqrt[3]{18}} - \frac{\sqrt[3]{\frac{2}{3}} \alpha }{t} \right ) + \beta\\
&= \frac{t^3}{18} - \frac{t \alpha}{\sqrt[3]{18}} + \frac{\frac{2}{3} \alpha^3 }{t^3} + \frac{\alpha t}{\sqrt[3]{18}} + \beta\\
&= \frac{t^3}{18} + \frac{\frac{2}{3} \alpha^3 }{t^3} + \beta\\
\end{align}
\todo[inline]{verify this solution}
\goodbreak
So the solution is given by
\begin{align*}
x_S &:= - \frac{b}{2a} \;\;\;\;\; \text{(the symmetry axis)}\\
\underset{x\in\mdr}{\arg \min d_{P,f}(x)} &= \begin{cases}
x_1 = +\sqrt{a (y_p + \frac{b^2}{4a} - c) - \frac{1}{2}} + x_S \text{ and } &\text{if } x_P = x_S \text{ and } y_p + \frac{b^2}{4a} - c > \frac{1}{2a} \\
x_2 = -\sqrt{a (y_p + \frac{b^2}{4a} - c) - \frac{1}{2}} + x_S\\
x_1 = x_S &\text{if } x_P = x_S \text{ and } y_p + \frac{b^2}{4a} - c \leq \frac{1}{2a} \\
x_1 = todo &\text{if } x_P \neq x_S
\end{cases}
\end{align*}
\clearpage
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Cubic %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Minimal distance to a cubic function}
Let $f(x) = a \cdot x^3 + b \cdot x^2 + c \cdot x + d$ be a cubic function
with $a \in \mdr \setminus \Set{0}$ and
$b, c, d \in \mdr$ be a function.
\begin{figure}[htp]
\centering
\begin{tikzpicture}
\begin{axis}[
legend pos=south east,
axis x line=middle,
axis y line=middle,
grid = major,
width=0.8\linewidth,
height=8cm,
grid style={dashed, gray!30},
xmin=-3, % start the diagram at this x-coordinate
xmax= 3, % end the diagram at this x-coordinate
ymin=-3, % start the diagram at this y-coordinate
ymax= 3, % end the diagram at this y-coordinate
axis background/.style={fill=white},
xlabel=$x$,
ylabel=$y$,
tick align=outside,
minor tick num=-3,
enlargelimits=true,
tension=0.08]
\addplot[domain=-3:3, thick,samples=50, red] {x*x*x};
\addplot[domain=-3:3, thick,samples=50, green] {x*x*x+x*x};
\addplot[domain=-3:3, thick,samples=50, blue] {x*x*x+2*x*x};
\addplot[domain=-3:3, thick,samples=50, orange] {x*x*x+x};
\addlegendentry{$f_1(x)=x^3$}
\addlegendentry{$f_2(x)=x^3 + x^2$}
\addlegendentry{$f_2(x)=x^3 + 2 \cdot x^2$}
\addlegendentry{$f_1(x)=x^3 + x$}
\end{axis}
\end{tikzpicture}
\caption{Cubic functions}
\end{figure}
%
%\subsection{Special points}
%\todo[inline]{Write this}
%
%\subsection{Voronoi}
%
%For $b^2 \geq 3ac$
%
%\todo[inline]{Write this}
\subsection{Calculate points with minimal distance}
\begin{theorem}
There cannot be an algebraic solution to the problem of finding
a closest point $(x, f(x))$ to a given point $P$ when $f$ is
a polynomial function of degree $3$ or higher.
\end{theorem}
\begin{proof}
Suppose you could solve the closest point problem for arbitrary
cubic functions $f = ax^3 + bx^2 + cx + d$ and arbitrary points $P = (x_P, y_P)$.
Then you could solve the following problem for $x$:
\begin{align}
0 &\stackrel{!}{=} \left ((d_{P,f}(x))^2 \right )'
&=-2 x_p + 2x -2y_p(f(x))' + (f(x)^2)'\\
&= 2 f(x) \cdot f'(x) - 2 y_p f'(x) + 2x - 2 x_p\\
&= f(x) \cdot f'(x) - y_p f'(x) + x - x_p\\
&= \underbrace{f'(x) \cdot \left (f(x) - y_p \right )}_{\text{Polynomial of degree 5}} + x - x_p
\end{align}
General algebraic equations of degree 5 don't have a solution formula.\footnote{TODO: Quelle}
Although here seems to be more structure, the resulting algebraic
equation can be almost any polynomial of degree 5:\footnote{Thanks to Peter Košinár on \href{http://math.stackexchange.com/a/584814/6876}{math.stackexchange.com} for this one}
\begin{align}
0 &\stackrel{!}{=} f'(x) \cdot \left (f(x) - y_p \right ) + (x - x_p)\\
&= \underbrace{3 a^2}_{= \tilde{a}} x^5 + \underbrace{5ab}_{\tilde{b}}x^4 + \underbrace{2(2ac + b^2 )}_{=: \tilde{c}}x^3 &+& \underbrace{3(ad+bc-ay_p)}_{\tilde{d}} x^2 \\
& &+& \underbrace{(2 b d+c^2+1-2 b y_p)}_{=: \tilde{e}}x+\underbrace{c d-c y_p-x_p}_{=: \tilde{f}}\\
0 &\stackrel{!}{=} \tilde{a}x^5 + \tilde{b}x^4 + \tilde{c}x^3 + \tilde{d}x^2 + \tilde{e}x + \tilde{f}
\end{align}
\begin{enumerate}
\item For any coefficient $\tilde{a} \in \mdr_{> 0}$ of $x^5$ we can choose $a$ such that we get $\tilde{a}$.
\item For any coefficient $\tilde{b} \in \mdr \setminus \Set{0}$ of $x^4$ we can choose $b$ such that we get $\tilde{b}$.
\item With $c$, we can get any value of $\tilde{c} \in \mdr$.
\item With $d$, we can get any value of $\tilde{d} \in \mdr$.
\item With $y_p$, we can get any value of $\tilde{e} \in \mdr$.
\item With $x_p$, we can get any value of $\tilde{f} \in \mdr$.
\end{enumerate}
The first restriction guaratees that we have a polynomial of
degree 5. The second one is necessary, to get a high range of
$\tilde{e}$.
This means, that there is no solution formula for the problem of
finding the closest points on a cubic function to a given point,
because if there was one, you could use this formula for finding
roots of polynomials of degree 5. $\qed$
\end{proof}
\subsection{Another approach}
Just like we moved the function $f$ and the point to get in a
nicer situation, we can apply this approach for cubic functions.
\begin{figure}[htp]
\centering
\begin{tikzpicture}
\begin{axis}[
legend pos=south east,
axis x line=middle,
axis y line=middle,
grid = major,
width=0.8\linewidth,
height=8cm,
grid style={dashed, gray!30},
xmin=-3, % start the diagram at this x-coordinate
xmax= 3, % end the diagram at this x-coordinate
ymin=-3, % start the diagram at this y-coordinate
ymax= 3, % end the diagram at this y-coordinate
axis background/.style={fill=white},
xlabel=$x$,
ylabel=$y$,
tick align=outside,
minor tick num=-3,
enlargelimits=true,
tension=0.08]
\addplot[domain=-3:3, thick,samples=50, red] {x*x*x};
\addplot[domain=-3:3, thick,samples=50, green] {x*x*x+x};
\addplot[domain=-3:3, thick,samples=50, orange] {x*x*x-x};
\addplot[domain=-3:3, thick,samples=50, blue, dotted] {x*x*x+2*x};
\addplot[domain=-3:3, thick,samples=50, lime, dashed] {x*x*x+3*x};
\addlegendentry{$f_1(x)=x^3$}
\addlegendentry{$f_2(x)=x^3 + x$}
\addlegendentry{$f_1(x)=x^3 - x$}
\addlegendentry{$f_2(x)=x^3 + 2 \cdot x$}
\addlegendentry{$f_2(x)=x^3 + 3 \cdot x$}
\end{axis}
\end{tikzpicture}
\caption{Cubic functions with $b = d = 0$}
\end{figure}
First, we move $f_0$ by $\frac{b}{3a}$ to the right, so
\[f_1(x) = ax^3 + \frac{b^2 (c-1)}{3a} x + \frac{2b^3}{27 a^2} - \frac{bc}{3a} + d \;\;\;\text{ and }\;\;\;P_1 = (x_P + \frac{b}{3a}, y_P)\]
because
\begin{align}
f_1(x) &= a \left (x - \frac{b}{3a} \right )^3 + b \left (x-\frac{b}{3a} \right )^2 + c \left (x-\frac{b}{3a} \right ) + d\\
&= a \left (x^3 - 3 \frac{b}{3a}x^2 + 3 (\frac{b}{3a})^2 x - \frac{b^3}{27a^3} \right )
+b \left (x^2 - \frac{2b}{3a} x + \frac{b^2}{9a^2} \right )
+c x - \frac{bc}{3a} + d\\
&= ax^3 - bx^2 + \frac{b^2}{3a}x - \frac{b^3}{27 a^2}\\
& \;\;\;\;\;\;+ bx^2 - \frac{2b^2}{3a}x + \frac{b^3}{9a^2}\\
& \;\;\;\;\;\;\;\;\;\;\;\; + c x - \frac{bc}{3a} + d\\
&= ax^3 + \frac{b^2}{3a}\left (1-2+c \right )x + \frac{b^3}{9a^2} \left (1-\frac{1}{3} \right )- \frac{bc}{3a} + d
\end{align}
\todo[inline]{Which way to move might be clever?}
\subsection{Number of points with minimal distance}
As there is an algebraic equation of degree 5, there cannot be more
than 5 solutions.
\todo[inline]{Can there be 3, 4 or even 5 solutions? Examples!
After looking at function graphs of cubic functions, I'm pretty
sure that there cannot be 4 or 5 solutions, no matter how you
chose the cubic function $f$ and $P$.
I'm also pretty sure that there is no polynomial (no matter what degree)
that has more than 3 solutions.}
\section{Newtons method}
\todo[inline]{When does Newtons method converge? How fast?
How to choose starting point?}
\section{Quadratic minimization}
\todo[inline]{TODO}
\section{Conclusion}
\todo[inline]{TODO}
\end{document}
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