slight formatting for aesthetics

PythonEssentials/MatplotlibIntro/MatplotlibIntro.tex

@@ -433,9 +433,7 @@ \section*{Other Kinds of Plots} % =============================================
 
 # Draw a histogram to display the distribution of the data in x.
 >>> ax2 = plt.subplot(122)
->>> ax2.hist(x, bins=np.arange(-4.5, 5.5))      # Or, equivalently,
-#   ax2.hist(x, bins=9, range=[-4.5, 4.5])
-
+>>> ax2.hist(x, bins=np.arange(-4.5, 5.5))      # Or bins=9, range=[-4.5, 4.5].
 >>> plt.show()
 \end{lstlisting}
 

Volume1/ComplexFunctions/ComplexFunctions.tex

@@ -117,7 +117,7 @@ \section*{Complex Functions} % ================================================
     \centering
     \includegraphics[width=\textwidth]{figures/discontinuity_magnitude.png}
 \end{subfigure}
-\caption{Plots of $f(z) = \sqrt{z^2+1}$ on the domain $\{x+iy \mid x,y \in [-3,3]\}$.
+\caption{Plots of $f(z) = \sqrt{z^2+1}$ on $\{x+iy \mid x,y \in [-3,3]\}$.
 Notice how a discontinuity is clearly visible in the angle plot on the left, but disappears from the magnitude plot on the right.}
 \label{fig:complex-discontinuity}
 \end{figure}

Volume1/ImportanceSampling/ImportanceSampling.tex

@@ -164,6 +164,8 @@ \subsection*{Choosing the Importance Distribution} % --------------------------
 However, a draw greater than 3 from the importance distribution is accompanied by a low importance weight because it is so unlikely in the target distribution.
 Similarly, a draw less than 3 from the importance distribution has a higher importance weight because it is more likely in the target distribution.
 
+\newpage
+
 \begin{problem} \label{prob:mc_important}
 Write a function that accepts a function handle $f$, representing the equation for the target distribution, a function handle $g$, representing the equation for the importance distribution, an indicator function $h$, a function that samples from the importance distribution, and an integer $n$ representing the number of samples to use.
 Use (\ref{eq:importance}) to approximate the integral of the target distribution and return this approximation.

Volume1/IterativeSolvers/IterativeSolvers.tex

@@ -76,7 +76,7 @@ \section*{The Jacobi Method} % ================================================
     %\hline
     $\x^{(2)}$  & 1.33333333 & 1.72222222 & -0.66666667 \\
     $\x^{(3)}$  & 1.16666667 & 1.88888889 & -0.90740741 \\
-    $\x^{(4)}$  & 1.04629630 & 1.99382716 & -0.96296296 \\
+    % $\x^{(4)}$  & 1.04629630 & 1.99382716 & -0.96296296 \\
     \vdots      & \vdots     & \vdots     & \vdots      \\
     $\x^{(28)}$ & 0.99999999 & 2.00000001 & -0.99999999 \\
     $\x^{(29)}$ & 1          & 2          & -1          \\

Volume1/QR_Decomposition/QR_Decomposition.tex

@@ -404,7 +404,6 @@ \subsection*{Householder Triangularization} % ---------------------------------
 \begin{problem} % QR Decomposition via Householder Triangularization
 Write a function that accepts as input a $m \times n$ matrix $A$ of rank $n$.
 Use Algorithm \ref{Alg:QR-via-Householder} to compute the full QR decomposition of $A$.
-
 Consider the following implementation details.
 \begin{itemize}