Change n=6 to n=10

latex/problems/problem-6.tex

@@ -1,18 +1,18 @@
 \section*{Problem 6}
 
 \subsection*{a)}
-The plot in Figure \ref{fig:eigenvector_6} is showing the discretization of $\hat{x}$ with $n=6$. 
+The plot in Figure \ref{fig:eigenvector_10} is showing the discretization of $\hat{x}$ with $n=10$. 
 The eigenvectors and corresponding analytical eigenvectors have a complete overlap suggesting the implementation of the algorithm is correct. 
 We have included the boundary points for each vector to show a complete solution.
 \begin{figure}[h]
     \centering
-    \includegraphics[width=0.8\textwidth]{images/eigenvector_6.pdf}
-    \caption{The plot is showing the elements of eigenvector $\vec{v}_{1}, \vec{v}_{2}, \vec{v}_{3}$, corresponding to the three lowest eigenvalues of matrix $\boldsymbol{A} (6 \cross 6)$, against the position $\hat{x}$. The analytical eigenvectors $\vec{v}^{(1)}, \vec{v}^{(2)}, \vec{v}^{(3)}$ are also included in the plot.}
-    \label{fig:eigenvector_6}
+    \includegraphics[width=0.8\textwidth]{images/eigenvector_10.pdf}
+    \caption{The plot is showing the elements of eigenvector $\vec{v}_{1}, \vec{v}_{2}, \vec{v}_{3}$, corresponding to the three lowest eigenvalues of matrix $\boldsymbol{A} (10 \cross 10)$, against the position $\hat{x}$. The analytical eigenvectors $\vec{v}^{(1)}, \vec{v}^{(2)}, \vec{v}^{(3)}$ are also included in the plot.}
+    \label{fig:eigenvector_10}
 \end{figure}
 
 \subsection*{b)}
-For the discretization with $n=100$ the solution is visually close to a continous curve, with a complete overlap of the analytical eigenvectors, presented in Figure \ref{fig:eigenvector_100}.
+For the discretization with $n=100$ the solution is visually close to a continuous curve, with a complete overlap of the analytical eigenvectors, presented in Figure \ref{fig:eigenvector_100}.
 \begin{figure}
     \centering
     \includegraphics[width=0.8\textwidth]{images/eigenvector_100.pdf}

src/main.cpp

@@ -156,9 +156,9 @@ void write_eigenvec(int N)
 
 int main()
 {
-    // write_transformation_tridiag(100);
-    // write_transformation_dense(100);
-    write_eigenvec(6);
+     write_transformation_tridiag(100);
+     write_transformation_dense(100);
+    write_eigenvec(10);
     write_eigenvec(100);
     return 0;
 }

src/plot.py

@@ -24,7 +24,6 @@ def plot_transformations(save: bool=False) -> None:
         fig.savefig("../latex/images/transform.pdf")
 
 
-
 def plot_eigenvectors(N: int, save: bool=False) -> None:
     # Load data based on matrix size
     path = f"../latex/output/eigenvector_{N}.csv"
@@ -46,10 +45,7 @@ def plot_eigenvectors(N: int, save: bool=False) -> None:
         fig.savefig(f"../latex/images/eigenvector_{N}.pdf")
 
      
-
-
 if __name__ == '__main__':
-    # plot_transformations(True)
-    plot_eigenvectors(6, True)
+    plot_transformations(True)
+    plot_eigenvectors(10, True)
     plot_eigenvectors(100, True)
-    # plt.show()