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\begin{document}
    
    
    

    
    \section{Innlevering 4 - Numerisk
integrasjon}\label{innlevering-4---numerisk-integrasjon}

    Standardnormalfordelingen er gitt ved \[
    f(x) = \frac{1}{\sqrt{2\pi}} e^{-x ^2/2}.
\]

    Standardnormalfordelingen er et eksempel på en
sannsynlighetstetthetsfunksjon, og for TMA4400 holder det å vite at
sannsynligheten for å havne mellom \(a\) og \(b\) er gitt ved \[
\frac{1}{\sqrt{2\pi}} \int_a^b e^{-x ^2/2} \; dx.
\] Det er bare ett problem - standardnormalfordelingen kan ikke
antideriveres. Under er kode for å beregne arealet ved riemannsummer.

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\begin{Verbatim}[commandchars=\\\{\}]
\PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np}

\PY{c+c1}{\PYZsh{}integrasjonsgrenser}
\PY{n}{a}\PY{o}{=}\PY{l+m+mi}{0}
\PY{n}{b}\PY{o}{=}\PY{l+m+mi}{1}

\PY{c+c1}{\PYZsh{}gitter}
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\PY{n}{h}\PY{o}{=}\PY{n}{b}\PY{o}{/}\PY{n}{n} 

\PY{c+c1}{\PYZsh{}venstre riemannsum}
\PY{n}{f}\PY{o}{=}\PY{n}{h}\PY{o}{*}\PY{p}{(}\PY{n}{np}\PY{o}{.}\PY{n}{sum}\PY{p}{(}\PY{n}{np}\PY{o}{.}\PY{n}{exp}\PY{p}{(}\PY{o}{\PYZhy{}}\PY{n}{x}\PY{o}{*}\PY{o}{*}\PY{l+m+mi}{2}\PY{o}{/}\PY{l+m+mi}{2}\PY{p}{)}\PY{p}{)}\PY{o}{\PYZhy{}}\PY{n}{np}\PY{o}{.}\PY{n}{exp}\PY{p}{(}\PY{o}{\PYZhy{}}\PY{n}{b}\PY{o}{*}\PY{o}{*}\PY{l+m+mi}{2}\PY{o}{/}\PY{l+m+mi}{2}\PY{p}{)}\PY{p}{)}\PY{o}{/}\PY{n}{np}\PY{o}{.}\PY{n}{sqrt}\PY{p}{(}\PY{l+m+mi}{2}\PY{o}{*}\PY{n}{np}\PY{o}{.}\PY{n}{pi}\PY{p}{)}

\PY{n+nb}{print}\PY{p}{(}\PY{n}{f}\PY{p}{)}
\end{Verbatim}
\end{tcolorbox}

    \begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\tightlist
\item
  Modifiser koden til å beregne arealet ved trapesmetoden, kjør alt med
  \(a=0\), \(b=1\), sammenlikne begge metoder med den korrekte
  sannsynligheten \[
  \frac{1}{\sqrt{2\pi}} \int_0^1 e^{-x ^2/2} \; dx \approx 0.3413447460685429485852.
  \] Kan du forklare forskjellen? (Fun fact: Det er denne beregningen
  som forteller deg at om du har IQ på 115 er du smartere enn om lag 84
  prosent av befolkningen. Mer om dette i TMA4240/4245.)
\end{enumerate}

    \begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\setcounter{enumi}{1}
\tightlist
\item
  Man kan bruke polynominterpolasjon til å finne bedre teknikker for
  numerisk integrasjon. Utled Simpsons metode ved å finne
  lagrangepolynomene til gitteret \[
   \left\{ a, \frac{a+b}{2}, b \right\}
  \] og integrere dem. Test Simpsons metode på arealet over og forklar
  forskjellen.
\end{enumerate}

    De numeriske integrasjonsrutinene gir oss et sett med korresponderende
teknikker for å løse ordinære differensiallikninger numerisk. La oss si
vi ønsker å løse initialverdiproblemet \[
    \dot x = f\left( x \right) \hspace{10mm} x(0) = 0
\] på intervallet \([0,T]\). Vi deler tidsintervallet inn i en partisjon
med \(n+1\) punkter \[
    t_k = kh \hspace{10mm} 0\leq k \leq n
\] der \(h=T/n\), og integrerer differensiallikningen fra ett
gitterpunkt til neste: \[
    x\left( t_{k+1} \right) - x\left( t_k \right) = \int_{t_k}^{t_{k+1}} f\left( x\left( t \right) \right) \; dt
\] Hvis vi setter inn en enkelt rektangulær riemannboks (venstre, høyre
eller sentrert) eller trapesmetoden for integralet på høyresiden, og så
setter inn approksimasjonen \(x_k \approx x\left( t_k \right)\) overalt,
får vi nå fire metoder på rappen: \begin{align*}
    \text{Fremlengs Euler:} \hspace{10mm} &x_{k+1} = x_k + hf\left( x_k \right) \\[3mm]
    \text{Baklengs Euler:} \hspace{10mm} &x_{k+1} = x_k + hf\left( x_{k+1} \right) \\[3mm]
    \text{Midtpunktmetoden:} \hspace{10mm} &x_{k+1} = x_k + hf\left( \frac{x_k + x_{k+1}}{2} \right) \\[3mm]
    \text{Trapesmetoden:} \hspace{10mm} &x_{k+1} = x_k + \frac{h}{2}\left( f\left( x_k \right) + f\left( x_{k+1} \right) \right)
\end{align*} Under finner du kode for fremlengs Euler på det logistiske
problemet \[
\dot{x}=a\left( 1-x/k \right)x \hspace{10mm} x(0) = x_0 \hspace{10mm} a >0,\; k > 0.
\]

    \begin{tcolorbox}[breakable, size=fbox, boxrule=1pt, pad at break*=1mm,colback=cellbackground, colframe=cellborder]
\prompt{In}{incolor}{ }{\boxspacing}
\begin{Verbatim}[commandchars=\\\{\}]
\PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt}

\PY{c+c1}{\PYZsh{}parametre}
\PY{n}{a}\PY{o}{=}\PY{l+m+mi}{1} \PY{c+c1}{\PYZsh{}vekstfaktor}
\PY{n}{k}\PY{o}{=}\PY{l+m+mi}{1} \PY{c+c1}{\PYZsh{}bærekapasitet}

\PY{c+c1}{\PYZsh{}tidsakse}
\PY{n}{T}\PY{o}{=}\PY{l+m+mi}{10}
\PY{n}{N}\PY{o}{=}\PY{l+m+mi}{10000}
\PY{n}{h}\PY{o}{=}\PY{n}{T}\PY{o}{/}\PY{n}{N}
\PY{n}{t}\PY{o}{=}\PY{n}{np}\PY{o}{.}\PY{n}{linspace}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{,}\PY{n}{T}\PY{p}{,}\PY{n}{N}\PY{o}{+}\PY{l+m+mi}{1}\PY{p}{)}

\PY{c+c1}{\PYZsh{}løsning}
\PY{n}{x}\PY{o}{=}\PY{n}{np}\PY{o}{.}\PY{n}{zeros}\PY{p}{(}\PY{n}{N}\PY{o}{+}\PY{l+m+mi}{1}\PY{p}{)}
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\PY{k}{for} \PY{n}{i} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{n}{N}\PY{p}{)}\PY{p}{:}
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\PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{t}\PY{p}{,}\PY{n}{x}\PY{p}{)}    
\PY{n}{plt}\PY{o}{.}\PY{n}{savefig}\PY{p}{(}\PY{l+s+s1}{\PYZsq{}}\PY{l+s+s1}{logistisk.png}\PY{l+s+s1}{\PYZsq{}}\PY{p}{)} 
\end{Verbatim}
\end{tcolorbox}

    \begin{enumerate}
\def\labelenumi{\arabic{enumi}.}
\setcounter{enumi}{2}
\tightlist
\item
  Implementér de forskjellige metodene for det logistiske problemet og
  sammenlikne med den analytiske løsningen \[
  x(t)=\frac{kx_0e^{at}}{k-x_0+x_0e^{at}}.
  \] Kan du forklare forskjellene? (Velg vekstfaktoren \(a\) og
  bærekapasiteten \(k\) selv. For midtpunkt- og trapesmetoden kan en
  numerisk likningsløser komme til nytte.)
\end{enumerate}


    % Add a bibliography block to the postdoc
    
    
    
\end{document}
