feat: Adding Results to the raport

This commit is contained in:
PolishPigeon 2022-01-08 06:11:08 +01:00
parent 245acae5b6
commit f9c270a61c
54 changed files with 105499 additions and 179 deletions

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Chapter 2.
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\BOOKMARK [2][-]{subsection.1.2.1}{Linear Least Squares Problem}{section.1.2}% 4
\BOOKMARK [2][-]{subsection.1.2.2}{Gram matrix and QR decomposition}{section.1.2}% 5
\BOOKMARK [2][-]{subsection.1.2.3}{Gram-Schmidt algorithm}{section.1.2}% 6
\BOOKMARK [0][-]{chapter.2}{Determine trajectory of the motion}{}% 7
\BOOKMARK [1][-]{section.2.1}{a\) Runge-Kutta method of 4th order and Adams PC}{chapter.2}% 8
\BOOKMARK [2][-]{subsection.2.1.1}{Problem}{section.2.1}% 9
\BOOKMARK [2][-]{subsection.2.1.2}{Theoretical Introdution}{section.2.1}% 10
\BOOKMARK [2][-]{subsection.2.1.3}{Adams PC method}{section.2.1}% 11
\BOOKMARK [1][-]{section.2.2}{b\) Runge-Kutta method of 4th order with variable step size automatically adjusted}{chapter.2}% 12
\BOOKMARK [2][-]{subsection.2.2.1}{Problem}{section.2.2}% 13
\BOOKMARK [2][-]{subsection.2.2.2}{Theoretical Introdution}{section.2.2}% 14
\BOOKMARK [1][-]{section.1.3}{Results}{chapter.1}% 7
\BOOKMARK [0][-]{chapter.2}{Determine trajectory of the motion}{}% 8
\BOOKMARK [1][-]{section.2.1}{a\) Runge-Kutta method of 4th order and Adams PC}{chapter.2}% 9
\BOOKMARK [2][-]{subsection.2.1.1}{Problem}{section.2.1}% 10
\BOOKMARK [2][-]{subsection.2.1.2}{Theoretical Introdution}{section.2.1}% 11
\BOOKMARK [2][-]{subsection.2.1.3}{Adams PC method}{section.2.1}% 12
\BOOKMARK [1][-]{section.2.2}{b\) Runge-Kutta method of 4th order with variable step size automatically adjusted}{chapter.2}% 13
\BOOKMARK [2][-]{subsection.2.2.1}{Problem}{section.2.2}% 14
\BOOKMARK [2][-]{subsection.2.2.2}{Theoretical Introdution}{section.2.2}% 15
\BOOKMARK [1][-]{section.2.3}{Results}{chapter.2}% 16

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@ -151,6 +151,88 @@ Then we get a very well conditioned set of normal equations:
\[ \vdots \]
\[ a_n\langle \psi_n, \psi_n \rangle = \langle f, \psi_n \rangle \]
\section{Results}
I decided to simulate polynomials up to $\mathbf{10^{th}}$ degree.
\begin{center}
\includegraphics[scale=0.25]{10.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{11.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{12.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{13.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{14.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{15.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{16.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{17.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{18.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{19.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{110.eps}
\end{center}
\begin{center}
\begin{tabular}{| c | c | c |}
\hline
Degree & Error & Condition number of Gram's Matrix \\
\hline
0& 13.2040& 1\\
\hline
1& 9.1281& 10\\
\hline
2& 8.8257& 408.7796\\
\hline
3& 4.2365& 8.5584e+03\\
\hline
4& 1.5418& 3.1798e+05\\
\hline
5& 1.5413& 7.4675e+06\\
\hline
6& 1.1689& 2.8316e+08\\
\hline
7& 0.9345& 7.6462e+09\\
\hline
8& 0.8883& 3.3055e+11\\
\hline
9& 0.8334& 1.5167e+13\\
\hline
10& 6.3645e-13& 9.2930e+14\\
\hline
\end{tabular}
\end{center}
@ -267,6 +349,48 @@ For RK methods we have following parameters:
\[ \epsilon_i = |(y_i)_n^{(2)}| \epsilon_r + \epsilon_a \]
\[ \alpha = \min_{1 \leq i \leq m} (\frac{\epsilon_i}{|\delta_n(h)_i}|)^{\frac{1}{p+1}} \]
\section{Results}
\begin{center}
\includegraphics[scale=0.25]{20.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{21.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{22.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{23.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{24.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{25.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{26.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{27.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{28.eps}
\end{center}
\begin{center}
\includegraphics[scale=0.25]{29.eps}
\end{center}
\begin{thebibliography}{9}
\bibitem{texbook}

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@ -4,14 +4,16 @@
\contentsline {subsection}{\numberline {1.2.1}Linear Least Squares Problem}{3}{subsection.1.2.1}%
\contentsline {subsection}{\numberline {1.2.2}Gram matrix and QR decomposition}{3}{subsection.1.2.2}%
\contentsline {subsection}{\numberline {1.2.3}Gram-Schmidt algorithm}{4}{subsection.1.2.3}%
\contentsline {chapter}{\numberline {2}Determine trajectory of the motion}{6}{chapter.2}%
\contentsline {section}{\numberline {2.1}a) Runge-Kutta method of $4^{th}$ order and Adams PC}{6}{section.2.1}%
\contentsline {subsection}{\numberline {2.1.1}Problem}{6}{subsection.2.1.1}%
\contentsline {subsection}{\numberline {2.1.2}Theoretical Introdution}{6}{subsection.2.1.2}%
\contentsline {subsubsection}{Runge-Kutta method of order 4}{7}{section*.2}%
\contentsline {subsection}{\numberline {2.1.3}Adams PC method}{7}{subsection.2.1.3}%
\contentsline {section}{\numberline {2.2}b) Runge-Kutta method of $4^{th}$ order with variable step size automatically adjusted}{8}{section.2.2}%
\contentsline {subsection}{\numberline {2.2.1}Problem}{8}{subsection.2.2.1}%
\contentsline {subsection}{\numberline {2.2.2}Theoretical Introdution}{8}{subsection.2.2.2}%
\contentsline {subsubsection}{Error estimation using step doubling-approach}{8}{section*.3}%
\contentsline {subsubsection}{Actual correction of the step size}{10}{section*.4}%
\contentsline {section}{\numberline {1.3}Results}{5}{section.1.3}%
\contentsline {chapter}{\numberline {2}Determine trajectory of the motion}{12}{chapter.2}%
\contentsline {section}{\numberline {2.1}a) Runge-Kutta method of $4^{th}$ order and Adams PC}{12}{section.2.1}%
\contentsline {subsection}{\numberline {2.1.1}Problem}{12}{subsection.2.1.1}%
\contentsline {subsection}{\numberline {2.1.2}Theoretical Introdution}{12}{subsection.2.1.2}%
\contentsline {subsubsection}{Runge-Kutta method of order 4}{13}{section*.2}%
\contentsline {subsection}{\numberline {2.1.3}Adams PC method}{13}{subsection.2.1.3}%
\contentsline {section}{\numberline {2.2}b) Runge-Kutta method of $4^{th}$ order with variable step size automatically adjusted}{14}{section.2.2}%
\contentsline {subsection}{\numberline {2.2.1}Problem}{14}{subsection.2.2.1}%
\contentsline {subsection}{\numberline {2.2.2}Theoretical Introdution}{14}{subsection.2.2.2}%
\contentsline {subsubsection}{Error estimation using step doubling-approach}{14}{section*.3}%
\contentsline {subsubsection}{Actual correction of the step size}{16}{section*.4}%
\contentsline {section}{\numberline {2.3}Results}{17}{section.2.3}%

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@ -5,8 +5,8 @@ function task1(polynomialDegree)
[LSPSolutions, error, conditionNumber] = approximate(dataPoints, currentPolynomialDegree);
displayInfo(currentPolynomialDegree, error, conditionNumber);
plotGraph(currentPolynomialDegree, dataPoints, LSPSolutions)
end
end
function displayInfo(currentPolynomialDegree, error, conditionNumber)

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@ -1,116 +0,0 @@
function task2()
[ordinaryDifferentialEquations, initialValues, interval, algorithms] = initialize();
solveAndPrintODEs(algorithms, ordinaryDifferentialEquations, initialValues, interval);
[result, sizes, errors] = solveAndPlotODEAutomatic(ordinaryDifferentialEquations, initialValues, interval);
plotStatistics(result, sizes, errors);
plotComparisonToMatlabFunction(ordinaryDifferentialEquations, interval, initialValues);
end
function [ordinaryDifferentialEquations, initialValues, interval, algorithms] = initialize()
ordinaryDifferentialEquations = {
@(x) x(2) + x(1) * (0.5 - x(1)^2 - x(2)^2);
@(x) -x(1) + x(2) * (0.5 - x(1)^2 - x(2)^2)
};
initialValues = [8; 9];
interval = [0; 15];
algorithms = {
'RK4 algorithm', @RK4, [0.01, 0.011];
'Adams PC algorithm', @AdamsPCMethod, [0.002, 0.01305]
};
end
function solveAndPrintODEs(algorithms, ordinaryDifferentialEquations, initialValues, interval)
for algorithm = 1 : 2
[algorithmName, algorithmFunction, stepSizes] = algorithms{algorithm, :};
stepResults = solveForEachStep(stepSizes, ordinaryDifferentialEquations, initialValues, interval, algorithmFunction);
plotAgainstTime(ordinaryDifferentialEquations, algorithmName, stepResults);
plotAgainst(algorithmName, stepResults);
end
end
function stepResults = solveForEachStep(stepSizes, ordinaryDifferentialEquations, initialValues, interval, algorithmFunction)
stepResults = cell(size(stepSizes, 2), 3);
stepNames = {'Optimal Step Size', 'Slightly Larger Step Size'};
for stepNumber = 1:size(stepSizes, 2)
result = algorithmFunction(ordinaryDifferentialEquations, initialValues, interval, stepSizes(stepNumber));
stepResults(stepNumber, :) = {stepSizes(stepNumber), stepNames{stepNumber}, result};
end
end
function beginPlot()
figure;
grid on;
hold on;
end
function plotAgainstTime(ordinaryDifferentialEquations, algorithmName, stepResults)
for equationNumber = 1:size(ordinaryDifferentialEquations, 1)
beginPlot();
title([algorithmName, ', x_', num2str(equationNumber), ' ODE']);
for stepresult = stepResults'
plot(stepresult{3}(1, :), stepresult{3}(equationNumber + 1, :));
end
hold off;
legend(stepResults{:, 2});
end
end
function plotAgainst(algorithmName, stepResults)
beginPlot();
title([algorithmName, ' trajectory plot (x_2 compared to x_1)']);
for stepresult = stepResults'
plot(stepresult{3}(2, :), stepresult{3}(3, :));
end
hold off;
legend(stepResults{:, 2});
% %print(['report/', func2str(algfunc), 'traj'], '-dpdf');
end
function [result, sizes, errors] = solveAndPlotODEAutomatic(ordinaryDifferentialEquations, initialValues, interval)
[result, sizes, errors] = solveRKAutomatic(ordinaryDifferentialEquations, initialValues, interval);
plotTrajectory(result);
end
function [result, sizes, errors] = solveRKAutomatic(ordinaryDifferentialEquations, initialValues, interval)
initialStepSize = 1e-5;
relativeEpsilon = 1e-9;
absoluteEpsilon = 1e-9;
[result, sizes, errors] = RK4Automatic(ordinaryDifferentialEquations, initialValues, interval, initialStepSize, relativeEpsilon, absoluteEpsilon);
end
function plotTrajectory(result)
figure;
plot(result(2, :), result(3, :));
grid on;
title('RK4 auto-step trajectory plot (x_2 against x_1)');
end
function plotStatistics(result, sizes, errors)
stats = {
"RK4 auto-step step size", "rk4sizes", sizes;
"RK4 auto-step approximation error", "rk4errors", errors
};
for stat = stats'
figure;
plot(result(1, 2:(end - 1)), stat{3});
grid on;
title(stat{1});
end
end
function plotComparisonToMatlabFunction(ordinaryDifferentialEquations, interval, initialValues)
% compare results with ODE45
odefun = @(t, x) [ ordinaryDifferentialEquations{1}(x); ordinaryDifferentialEquations{2}(x) ];
odeoptions = odeset('RelTol', 10e-10, 'AbsTol', 10e-10);
[~, x] = ode45(odefun, interval, initialValues, odeoptions);
figure;
plot(x(:, 1), x(:, 2));
grid on;
title('ODE45 trajectory plot (x_2 against x_1)');
end

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@ -17,7 +17,7 @@ function [ordinaryDifferentialEquations, initialValues, interval, algorithms] =
algorithms = {
'RK4 algorithm', @RK4, [0.01, 0.011];
'Adams PC algorithm', @AdamsPCMethod, [0.002, 0.01305]
'Adams PC algorithm', @AdamsPCMethod, [0.002, 0.013]
};
end