update

doc/BookChapters/BookProject/acknow.tex

@@ -0,0 +1,11 @@
+%%%%%%%%%%%%%%%%%%%%%%acknow.tex%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% sample acknowledgement chapter
+%
+% Use this file as a template for your own input.
+%
+%%%%%%%%%%%%%%%%%%%%%%%% Springer %%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\extrachap{Acknowledgements}
+
+Use the template \emph{acknow.tex} together with the Springer document class SVMono (monograph-type books) or SVMult (edited books) if you prefer to set your acknowledgement section as a separate chapter instead of including it as last part of your preface.
+

doc/BookChapters/BookProject/acronym.tex

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+%%%%%%%%%%%%%%%%%%%%%%acronym.tex%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% sample list of acronyms
+%
+% Use this file as a template for your own input.
+%
+%%%%%%%%%%%%%%%%%%%%%%%% Springer %%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\extrachap{Acronyms}
+
+Use the template \emph{acronym.tex} together with the Springer document class SVMono (monograph-type books) or SVMult (edited books) to style your list(s) of abbreviations or symbols in the Springer layout.
+
+Lists of abbreviations\index{acronyms, list of}, symbols\index{symbols, list of} and the like are easily formatted with the help of the Springer-enhanced \verb|description| environment.
+
+\begin{description}[CABR]
+\item[ABC]{Spelled-out abbreviation and definition}
+\item[BABI]{Spelled-out abbreviation and definition}
+\item[CABR]{Spelled-out abbreviation and definition}
+\end{description}

doc/BookChapters/BookProject/book.tex

@@ -420,10 +420,10 @@
 
 \frontmatter%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-%\include{dedic}
-%\include{foreword}
-%\include{preface}
-%\include{acknow}
+\include{dedic}
+\include{foreword}
+\include{preface}
+\include{acknow}
 
 \tableofcontents
 

doc/BookChapters/BookProject/dedic.tex

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+
+%%%%%%%%%%%%%%%%%%%%%%% dedic.tex %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% sample dedication
+%
+% Use this file as a template for your own input.
+%
+%%%%%%%%%%%%%%%%%%%%%%%% Springer %%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\begin{dedication}
+Use the template \emph{dedic.tex} together with the Springer document class SVMono for monograph-type books or SVMult for contributed volumes to style a quotation or a dedication\index{dedication} at the very beginning of your book in the Springer layout
+\end{dedication}
+
+
+
+

doc/BookChapters/BookProject/preface.tex

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+\preface
+%  last update : 24/8/2013  mhj
+
+\begin{quotation}
+So, ultimately, in order to understand nature it may be necessary to
+have a deeper understanding of mathematical relationships. But the
+real reason is that the subject is enjoyable, and although we humans
+cut nature up in different ways, and we have different courses in
+different departments, such compartmentalization is really artificial,
+and we should take our intellectual pleasures where we find them. 
+{\em Richard Feynman, The Laws of Thermodynamics.}
+\end{quotation}
+
+Why a preface you may ask? Isn't that just a mere exposition of a
+raison d'$\mathrm{\hat{e}}$tre of an author's choice of material,
+preferences, biases, teaching philosophy etc.?  To a large extent I
+can answer in the affirmative to that. A preface ought to be personal.
+Indeed, what you will see in the various chapters of these notes
+represents how I perceive computational physics should be taught.
+
+ This set of lecture notes serves the scope of presenting to you and
+train you in an algorithmic approach to problems in the sciences,
+represented here by the unity of three disciplines, physics,
+mathematics and informatics. This trinity outlines the emerging field
+of computational physics.
+
+Our insight in a physical system, combined with numerical mathematics
+gives us the rules for setting up an algorithm, viz.~a set of rules
+for solving a particular problem.  Our understanding of the physical
+system under study is obviously gauged by the natural laws at play,
+the initial conditions, boundary conditions and other external
+constraints which influence the given system. Having spelled out the
+physics, for example in the form of a set of coupled partial
+differential equations, we need efficient numerical methods in order
+to set up the final algorithm.  This algorithm is in turn coded into a
+computer program and executed on available computing facilities.  To
+develop such an algorithmic approach, you will be exposed to several
+physics cases, spanning from the classical pendulum to quantum
+mechanical systems. We will also present some of the most popular
+algorithms from numerical mathematics used to solve a plethora of
+problems in the sciences.  Finally we will codify these algorithms
+using some of the most widely used programming languages, presently C,
+C++ and Fortran and its most recent standard Fortran
+2008\footnote{Throughout this text we refer to Fortran 2008 as
+Fortran, implying the latest standard.}. However, a high-level and fully
+object-oriented language like Python is now emerging as a good
+alternative although C++ and Fortran still outperform Python when it
+comes to computational speed.  In this text we offer an approach where
+one can write all programs in C/C++ or Fortran.  We will also show you
+how to develop large programs in Python interfacing C++ and/or Fortran
+functions for those parts of the program which are CPU intensive.
+Such an approach allows you to structure the flow of data in a
+high-level language like Python while tasks of a mere repetitive and
+CPU intensive nature are left to low-level languages like C++ or
+Fortran. Python allows you also to smoothly interface your program
+with other software, such as plotting programs or operating system
+instructions. A typical Python program you may end up writing contains
+everything from compiling and running your codes to preparing the body
+of a file for writing up your report.
+
+
+
+Computer simulations are nowadays an integral part of contemporary
+basic and applied research in the sciences.  Computation is becoming
+as important as theory and experiment. In physics, computational
+physics, theoretical physics and experimental physics are all equally
+important in our daily research and studies of physical
+systems. Physics is the unity of theory, experiment and
+computation\footnote{We mentioned previously the trinity of physics,
+mathematics and informatics. Viewing physics as the trinity of theory,
+experiment and simulations is yet another example. It is obviously
+tempting to go beyond the sciences. History shows that triunes,
+trinities and for example triple deities permeate the Indo-European
+cultures (and probably all human cultures), from the ancient Celts and
+Hindus to modern days.  The ancient Celts revered many such trinues,
+their world was divided into earth, sea and air, nature was divided in
+animal, vegetable and mineral and the cardinal colours were red,
+yellow and blue, just to mention a few.  As a curious digression, it
+was a Gaulish Celt, Hilary, philosopher and bishop of Poitiers (AD
+315-367) in his work {\em De Trinitate} who formulated the Holy
+Trinity concept of Christianity, perhaps in order to accomodate
+millenia of human divination practice.}.  Moreover, the ability "to
+compute" forms part of the essential repertoire of research
+scientists. Several new fields within computational science have
+emerged and strengthened their positions in the last years, such as
+computational materials science, bioinformatics, computational
+mathematics and mechanics, computational chemistry and physics and so
+forth, just to mention a few.  These fields underscore the importance
+of simulations as a means to gain novel insights into physical
+systems, especially for those cases where no analytical solutions can
+be found or an experiment is too complicated or expensive to carry
+out.  To be able to simulate large quantal systems with many degrees
+of freedom such as strongly interacting electrons in a quantum dot
+will be of great importance for future directions in novel fields like
+nano-techonology.  This ability often combines knowledge from many
+different subjects, in our case essentially from the physical
+sciences, numerical mathematics, computing languages, topics from
+high-performace computing and some knowledge of computers.
+
+
+In 1999, when I started this course at the department of physics in
+Oslo, computational physics and computational science in general were
+still perceived by the majority of physicists and scientists as topics
+dealing with just mere tools and number crunching, and not as subjects
+of their own.  The computational background of most students enlisting
+for the course on computational physics could span from dedicated
+hackers and computer freaks to people who basically had never used a
+PC. The majority of undergraduate and graduate students had a very
+rudimentary knowledge of computational techniques and methods.
+Questions like 'do you know of better methods for numerical
+integration than the trapezoidal rule' were not uncommon. I do happen
+to know of colleagues who applied for time at a supercomputing centre
+because they needed to invert matrices of the size of $10^4\times
+10^4$ since they were using the trapezoidal rule to compute
+integrals. With Gaussian quadrature this dimensionality was easily
+reduced to matrix problems of the size of $10^2\times 10^2$, with much
+better precision.
+
+More than a decade later most students have now been exposed to a
+fairly uniform introduction to computers, basic programming skills and
+use of numerical exercises.  Practically every undergraduate student
+in physics has now made a Matlab or Maple simulation of for example
+the pendulum, with or without chaotic motion.  Nowadays most of you
+are familiar, through various undergraduate courses in physics and
+mathematics, with interpreted languages such as Maple, Matlab and/or
+Mathematica. In addition, the interest in scripting languages such as
+Python or Perl has increased considerably in recent years.  The modern
+programmer would typically combine several tools, computing
+environments and programming languages. A typical example is the
+following. Suppose you are working on a project which demands
+extensive visualizations of the results. To obtain these results, that
+is to solve a physics problems like obtaining the density profile of a
+Bose-Einstein condensate, you need however a program which is fairly
+fast when computational speed matters.  In this case you would most
+likely write a high-performance computing program using Monte Carlo
+methods in languages which are tailored for that. These are
+represented by programming languages like Fortran and C++.  However,
+to visualize the results you would find interpreted languages like
+Matlab or scripting languages like Python extremely suitable for your
+tasks.  You will therefore end up writing for example a script in
+Matlab which calls a Fortran or C++ program where the number crunching
+is done and then visualize the results of say a wave equation solver
+via Matlab's large library of visualization tools. Alternatively, you
+could organize everything into a Python or Perl script which does
+everything for you, calls the Fortran and/or C++ programs and performs
+the visualization in Matlab or Python. Used correctly, these tools,
+spanning from scripting languages to high-performance computing
+languages and vizualization programs, speed up your capability to
+solve complicated problems.  Being multilingual is thus an advantage
+which not only applies to our globalized modern society but to
+computing environments as well.  This text shows you how to use C++
+and Fortran as programming languages.
+
+There is however more to the picture than meets the eye.  Although
+interpreted languages like Matlab, Mathematica and Maple allow you
+nowadays to solve very complicated problems, and high-level languages
+like Python can be used to solve computational problems, computational
+speed and the capability to write an efficient code are topics which
+still do matter. To this end, the majority of scientists still use
+languages like C++ and Fortran to solve scientific problems.  When you
+embark on a master or PhD thesis, you will most likely meet these
+high-performance computing languages.  This course emphasizes thus the
+use of programming languages like Fortran, Python and C++ instead of
+interpreted ones like Matlab or Maple. You should however note that
+there are still large differences in computer time between for example
+numerical Python and a corresponding C++ program for many numerical
+applications in the physical sciences, with a code in C++ or Fortran
+being the fastest.
+
+Computational speed is not the only reason for this choice of
+programming languages. Another important reason is that we feel that
+at a certain stage one needs to have some insights into the algorithm
+used, its stability conditions, possible pitfalls like loss of
+precision, ranges of applicability, the possibility to improve the
+algorithm and taylor it to special purposes etc etc.  One of our major
+aims here is to present to you what we would dub 'the algorithmic
+approach', a set of rules for doing mathematics or a precise
+description of how to solve a problem. To device an algorithm and
+thereafter write a code for solving physics problems is a marvelous
+way of gaining insight into complicated physical systems. The
+algorithm you end up writing reflects in essentially all cases your
+own understanding of the physics and the mathematics (the way you
+express yourself) of the problem.  We do therefore devote quite some
+space to the algorithms behind various functions presented in the
+text. Especially, insight into how errors propagate and how to avoid
+them is a topic we would like you to pay special attention to. Only
+then can you avoid problems like underflow, overflow and loss of
+precision. Such a control is not always achievable with interpreted
+languages and canned functions where the underlying algorithm and/or
+code is not easily accesible.  Although we will at various stages
+recommend the use of library routines for say linear
+algebra\footnote{Such library functions are often taylored to a given
+machine's architecture and should accordingly run faster than user
+provided ones.}, our belief is that one should understand what the
+given function does, at least to have a mere idea.  With such a
+starting point, we strongly believe that it can be easier to develope
+more complicated programs on your own using Fortran, C++ or Python.
+
+We have several other aims as well, namely:
+\begin{itemize}
+\item We would like to give you  an opportunity to gain a 
+      deeper understanding of the physics you have learned in other
+      courses. In most courses one is normally confronted with simple
+      systems which provide exact solutions and mimic to a certain
+      extent the realistic cases. Many are however the comments like
+      'why can't we do something else than the particle in a box
+      potential?'.  In several of the projects we hope to present some
+      more 'realistic' cases to solve by various numerical
+      methods. This also means that we wish to give examples of how
+      physics can be applied in a much broader context than it is
+      discussed in the traditional physics undergraduate curriculum.
+\item To encourage you to "discover" physics in a way similar to how 
+researchers learn in the context of research.
+\item Hopefully also to introduce numerical methods and new areas of physics that 
+      can be studied with the methods discussed.
+\item To teach   structured programming in the context of doing science. 
+\item The projects we propose are meant to mimic to a certain extent 
+      the situation encountered during a thesis or project work. You
+      will tipically have at your disposal 2-3 weeks to solve
+      numerically a given project. In so doing you may need to do a
+      literature study as well. Finally, we would like you to write a
+      report for every project.
+\end{itemize}
+Our overall goal is to encourage you to learn about science through
+experience and by asking questions. Our objective is always
+understanding and the purpose of computing is further insight, not
+mere numbers!  Simulations can often be considered as
+experiments. Rerunning a simulation need not be as costly as rerunning
+an experiment.
+
+
+
+Needless to say, these lecture notes are upgraded continuously, from
+typos to new input.  And we do always benefit from your comments,
+suggestions and ideas for making these notes better.  It's through the
+scientific discourse and critics we advance.  Moreover, I have
+benefitted immensely from many discussions with fellow colleagues and
+students. In particular I must mention Hans Petter Langtangen, Anders
+Malthe-S\o renssen, Knut M\o rken and \O yvind Ryan, whose input
+during the last fifteen years has considerably improved these lecture
+notes.  Furthermore, the time we have spent and keep spending together
+on the Computing in Science Education project at the University, is
+just marvelous. Thanks so much. Concerning the Computing in Science
+Education initiative, you can read more
+at \url{http://www.mn.uio.no/english/about/collaboration/cse/}.
+
+
+Finally, I would like to add a petit note on referencing. These notes
+have evolved over many years and the idea is that they should end up
+in the format of a web-based learning environment for doing
+computational science. It will be fully free and hopefully represent a
+much more efficient way of conveying teaching material than
+traditional textbooks.  I have not yet settled on a specific format,
+so any input is welcome. At present however, it is very easy for me to
+upgrade and improve the material on say a yearly basis, from simple
+typos to adding new material.  When accessing the web page of the
+course, you will have noticed that you can obtain all source files for
+the programs discussed in the text.  Many people have thus written to
+me about how they should properly reference this material and whether
+they can freely use it. My answer is rather simple.  You are
+encouraged to use these codes, modify them, include them in
+publications, thesis work, your lectures etc.  As long as your use is
+part of the dialectics of science you can use this material freely.
+However, since many weekends have elapsed in writing several of these
+programs, testing them, sweating over bugs, swearing in front of a
+f*@?\%g code which didn't compile properly ten minutes before monday
+morning's eight o'clock lecture etc etc, I would dearly appreciate in
+case you find these codes of any use, to reference them properly. That
+can be done in a simple way, refer to M.~Hjorth-Jensen, {\em
+Computational Physics}, University of Oslo (2013). The weblink to the
+course should also be included. Hope it is not too much to ask
+for. Enjoy!