X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/book_gpu.git/blobdiff_plain/1a01129f963257afbf1ca4effbb4e6e1f378cefa..d53d37855452b853b3db760e528dee96179dbe08:/BookGPU/Chapters/chapter11/ch11.tex

diff --git a/BookGPU/Chapters/chapter11/ch11.tex b/BookGPU/Chapters/chapter11/ch11.tex
index 7e319fd..26cc14f 100644
--- 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}
-\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}
-\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}
 
@@ -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.
-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]