Split background preamble

chapters/2_background.tex

@@ -2,41 +2,10 @@
 \chapter{Background}\label{ch:background}
 %************************************************
 
+\input{chapters/2_background_preamble}
+
 \jip{TODO:\ Mention something about LCG.}
 
-\noindent{}A goal shared between all programming languages is to provide a certain level
-of abstraction: an assembly language allows you to abstract from the binary
-instructions and memory positions; Low-level imperial languages, like FORTRAN,
-were the first to allow you to abstract from the processor architecture of the
-target machine; and nowadays writing a program requires little knowledge of the
-actual workings of the hardware on which the program is executed.
-
-Freuder states that the ``Holy Grail'' of programming languages would be where
-the user merely states the problem, and the computer solves it and that
-\gls{constraint-modelling} is one of the biggest steps towards this goal to
-this day \autocite*{freuder-1997-holygrail}. Different from imperative (and
-even other declarative) languages, in a \cml{} the modeller does not describe
-how to solve the problem, but rather provides the problem requirements. You
-could say that a constraint model actually describes the solution to the
-problem.
-
-In a constraint model, instead of specifying the manner in which we can find
-the solution, we give a concise description of the problem. We describe what we
-already know, the \parameters{}, what we wish to know, the \variables{}, and
-the relationships that should exist between them, the \constraints{}.
-
-This type of combinatorial problem is typically called a \gls{csp}. The goal of
-a \gls{csp} is to find values for the \variables{} that satisfy the
-\constraints{} or prove that no such assignment exists. Many \cmls\ also
-support the modelling of \gls{cop}, where a \gls{csp} is augmented with a
-\gls{objective} \(z\). In this case the goal is to find a solution that
-satisfies all \constraints{} while minimising (or maximising) \(z\).
-
-Although a constraint model does not contain any instructions on how to find a
-suitable solution, these models can generally be given to a dedicated solving
-program, or \solver{} for short, that can find a solution that fits the
-requirements of the model.
-
 \begin{example}%
   \label{ex:back-knapsack}
 

chapters/2_background_preamble.tex

@@ -0,0 +1,32 @@
+\noindent{}A goal shared between all programming languages is to provide a certain level
+of abstraction: an assembly language allows you to abstract from the binary
+instructions and memory positions; Low-level imperial languages, like FORTRAN,
+were the first to allow you to abstract from the processor architecture of the
+target machine; and nowadays writing a program requires little knowledge of the
+actual workings of the hardware on which the program is executed.
+
+Freuder states that the ``Holy Grail'' of programming languages would be where
+the user merely states the problem, and the computer solves it and that
+\gls{constraint-modelling} is one of the biggest steps towards this goal to
+this day \autocite*{freuder-1997-holygrail}. Different from imperative (and
+even other declarative) languages, in a \cml{} the modeller does not describe
+how to solve the problem, but rather provides the problem requirements. You
+could say that a constraint model actually describes the solution to the
+problem.
+
+In a constraint model, instead of specifying the manner in which we can find
+the solution, we give a concise description of the problem. We describe what we
+already know, the \parameters{}, what we wish to know, the \variables{}, and
+the relationships that should exist between them, the \constraints{}.
+
+This type of combinatorial problem is typically called a \gls{csp}. The goal of
+a \gls{csp} is to find values for the \variables{} that satisfy the
+\constraints{} or prove that no such assignment exists. Many \cmls\ also
+support the modelling of \gls{cop}, where a \gls{csp} is augmented with a
+\gls{objective} \(z\). In this case the goal is to find a solution that
+satisfies all \constraints{} while minimising (or maximising) \(z\).
+
+Although a constraint model does not contain any instructions on how to find a
+suitable solution, these models can generally be given to a dedicated solving
+program, or \solver{} for short, that can find a solution that fits the
+requirements of the model.

chapters/5_incremental.tex

@@ -1,3 +1,4 @@
+%************************************************
 \chapter{Incremental Processing}\label{ch:incremental}
 %************************************************