corrections chap1

[book_gpu.git] / BookGPU / Chapters / chapter11 / ch11.tex

diff --git a/BookGPU/Chapters/chapter11/ch11.tex b/BookGPU/Chapters/chapter11/ch11.tex
index 7e319fdd1b465e098bf79b1e8d4ccc2aadc9c09c..26cc14fc8c23a09d759c98d7f5eef9b102f4f26c 100644 (file)
--- a/BookGPU/Chapters/chapter11/ch11.tex
+++ b/BookGPU/Chapters/chapter11/ch11.tex
@@ -28,14 +28,14 @@ The rest of the chapter is organised as follows. Section \ref{ch11:splines} disc
 \begin{figure}[h]
 \centering
 \includegraphics[angle=0,width=8cm]{Chapters/chapter11/gregory1_plot1.pdf}
 \begin{figure}[h]
 \centering
 \includegraphics[angle=0,width=8cm]{Chapters/chapter11/gregory1_plot1.pdf}

-\caption{Cubic spline (solid) and monotone quadratic spline (dashed) interpolating monotone data from \cite{Gregory1982}. Cubic spline fails to preserve monotonicity of the data.}
+\caption[Cubic spline (solid) and monotone quadratic spline (dashed) interpolating monotone data]{Cubic spline (solid) and monotone quadratic spline (dashed) interpolating monotone data from \cite{Gregory1982}. Cubic spline fails to preserve monotonicity of the data.}

 \label{ch11:fig1}
 \end{figure}
 
 \begin{figure}[h]
 \centering
 \includegraphics[angle=00,width=8cm]{Chapters/chapter11/gregory1_plot2_b.pdf}
 \label{ch11:fig1}
 \end{figure}
 
 \begin{figure}[h]
 \centering
 \includegraphics[angle=00,width=8cm]{Chapters/chapter11/gregory1_plot2_b.pdf}

-\caption{Hermite cubic spline (solid) and Hermite rational spline interpolating monotone data from \cite{Gregory1982} with non-negative prescribed slopes. Despite non-negative slopes, Hermite cubic spline is not monotone.}
+\caption[Hermite cubic spline (solid) and Hermite rational spline interpolating monotone data]{Hermite cubic spline (solid) and Hermite rational spline interpolating monotone data from \cite{Gregory1982} with non-negative prescribed slopes. Despite non-negative slopes, Hermite cubic spline is not monotone.}

 \label{ch11:fig2}
 \end{figure}
 
 \label{ch11:fig2}
 \end{figure}
 


@@ -491,6 +491,6 @@ with $\hat y(k,l)$ being the unrestricted maximum likelihood estimator of $y_k\l
 \section{Conclusion} \label{ch11:conc}
 
 We presented three GPU-based parallel algorithms for approximating monotone data: monotone quadratic spline, monotone Hermite rational spline and minimum lower sets algorithm for monotonizing noisy data. These tools are valuable in a number of applications that involve large data sets modeled by monotone nonlinear functions.
 \section{Conclusion} \label{ch11:conc}
 
 We presented three GPU-based parallel algorithms for approximating monotone data: monotone quadratic spline, monotone Hermite rational spline and minimum lower sets algorithm for monotonizing noisy data. These tools are valuable in a number of applications that involve large data sets modeled by monotone nonlinear functions.

-The source code of the package monospline is available from \texttt{www.deakin.edu.au/$\sim$ gleb/monospline.html }
+The source code of the package monospline is available from \texttt{www.deakin.edu.au/$\sim$gleb/monospline.html }

 
 \putbib[Chapters/chapter11/biblio11]
 
 \putbib[Chapters/chapter11/biblio11]