Switch glossary to an index
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\newacronym[see={[Glossary:]{gls-cbls}}]{cbls}{CBLS}{Constraint-Based Local Search\glsadd{gls-cbls}}
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\newacronym[see={[Glossary:]{gls-cp}}]{cp}{CP}{Constraint Programming\glsadd{gls-cp}}
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\newacronym[see={[Glossary:]{gls-cse}}]{cse}{CSE}{Common Subexpression Elimination\glsadd{gls-cse}}
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\newacronym[see={[Glossary:]{gls-csp}}]{csp}{CSP}{Constraint Satisfaction Problem\glsadd{gls-csp}}
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\newacronym[see={[Glossary:]{gls-cop}}]{cop}{COP}{Constraint Optimisation Problem\glsadd{gls-cop}}
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\newacronym[see={[Glossary:]{gls-lns}}]{lns}{LNS}{Large Neighbourhood Search\glsadd{gls-lns}}
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\newacronym[see={[Glossary:]{gls-mip}}]{mip}{MIP}{Mixed Integer Programming\glsadd{gls-mip}}
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\newacronym[see={[Glossary:]{gls-sat}}]{sat}{SAT}{Boolean Satisfiability\glsadd{gls-sat}}
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\newacronym[see={[Glossary:]{gls-cbls}}]{cbls}{CBLS\glsadd{gls-cbls}}{Constraint-Based Local Search}
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\newacronym[see={[Glossary:]{gls-clp}}]{clp}{CLP\glsadd{gls-clp}}{Constraint Logic Programming}
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\newacronym[see={[Glossary:]{gls-cp}}]{cp}{CP\glsadd{gls-cp}}{Constraint Programming}
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\newacronym[see={[Glossary:]{gls-cse}}]{cse}{CSE\glsadd{gls-cse}}{Common Subexpression Elimination}
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\newacronym[see={[Glossary:]{gls-csp}}]{csp}{CSP\glsadd{gls-csp}}{Constraint Satisfaction Problem}
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\newacronym[see={[Glossary:]{gls-cop}}]{cop}{COP\glsadd{gls-cop}}{Constraint Optimisation Problem}
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\newacronym[see={[Glossary:]{gls-lns}}]{lns}{LNS\glsadd{gls-lns}}{Large Neighbourhood Search}
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\newacronym[see={[Glossary:]{gls-mip}}]{mip}{MIP\glsadd{gls-mip}}{Mixed Integer Programming}
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\newacronym[see={[Glossary:]{gls-sat}}]{sat}{SAT\glsadd{gls-sat}}{Boolean Satisfiability}
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@ -12,173 +12,142 @@
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\newglossaryentry{gls-cbls}{
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name={constraint-based local search},
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description={\jip{todo}},
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description={},
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}
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\newglossaryentry{constraint}{
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name={constraint},
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description={A constraint is a relationship between two or more decision
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variables or problem parameters that has to present in any valid solution to a
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problem},
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description={},
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}
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\newglossaryentry{constraint-modelling}{
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name={constraint modelling},
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description={Constraint modelling is a technique used to describe
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combinatorial problems. In this paradigm the problem in terms of
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\glspl{variable} that have an unknown value, but are potentially
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subject to certain \glspl{constraint}},
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description={},
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}
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\newglossaryentry{gls-cp}{
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name={constraint programming},
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description={Constraint Programming (CP) is a paradigm used to solve
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combinatorial problems. Its distinctive features are the declarative way in
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which the user creates a problem description, in this thesis referred to as
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\gls{constraint-modelling}, and its backtracking search that employs
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\gls{propagation} and customisable search heuristics},
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description={},
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}
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\newglossaryentry{gls-clp}{
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name={constraint logic programming},
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description={},
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}
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\newglossaryentry{gls-cse}{
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name={common subexpression elimination},
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description={Common Subexpression Elimination (CSE) is a technique used in the
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evaluation of programming languages to avoid redoing the same work. A
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description of how CSE works in \cmls\ can be found in \cref{sec:4-cse}},
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description={},
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}
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\newglossaryentry{gls-csp}{
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name={constraint satisfaction problem},
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description={TODO},
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description={},
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}
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\newglossaryentry{gls-cop}{
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name={constraint optimisation problem},
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description={TODO},
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description={},
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}
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\newglossaryentry{variable}{
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name={decision variable},
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description={A decision variable is a value that is yet to be determined. A
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problem defined as a constraint model is solved by assigning a value to each
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variable that does not violate any constraints in the model and, in case of an
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optimisation problem, optimises the objective function},
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description={},
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}
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\newglossaryentry{domain}{
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name={domain},
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description={The domain of a \gls{variable} is the set of values that
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it can still take to arrive at a solution to the problem.},
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description={},
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}
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\newglossaryentry{flatzinc}{
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name={Flat\-Zinc},
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description={A subset of the \minizinc\ syntax that is used as input for
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\glspl{solver}},
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description={},
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}
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\newglossaryentry{global}{
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name={global constraint},
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description={A global constraint is a common \gls{constraint} pattern that can
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be described using simpler \glspl{constraint}. \Glspl{solver} sometimes provide
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dedicated algorithms or rewriting rules to better handle the global constraint},
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description={},
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}
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\newglossaryentry{linear-programming}{
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name={linear programming},
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description={Linear programming is a method to optimise an linear objective
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function under the condition of a set of constraints which are all in the form
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of linear equations},
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description={},
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}
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\newglossaryentry{gls-lns}{
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name={large neighbourhood search},
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description={Large Neighbourhood Search (LNS) is a meta-search algorithm that
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repeatedly restricts the search space, \ie applying a \gls{neighbourhood}, to
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quickly find better solutions to a problem},
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description={},
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}
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\newglossaryentry{meta-search}{
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name={meta-search},
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plural={meta-searches},
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description={A search approach that repeatedly solves constraint models},
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description={},
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}
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\newglossaryentry{microzinc}{
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name={Micro\-Zinc},
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description={TODO},
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description={},
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}
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\newglossaryentry{minisearch}{
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name={Mini\-Search},
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description={TODO},
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description={},
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}
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\newglossaryentry{minizinc}{
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name={Mini\-Zinc},
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description={A high-level \gls{constraint-modelling} language with an
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extensive library of \glspl{global}},
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description={},
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}
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\newglossaryentry{gls-mip}{
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name={Mixed Integer Programming},
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description={A form of \gls{linear-programming} where at least one of the variable
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can only take an integer value},
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description={},
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}
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\newglossaryentry{nanozinc}{
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name={Nano\-Zinc},
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description={TODO},
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description={},
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}
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\newglossaryentry{neighbourhood}{
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name={neighbourhood},
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description={A neighbourhood is a restriction of the search space of the
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\gls{solver}},
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description={},
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}
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\newglossaryentry{objective}{
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name={objective},
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description={The objective in a constraint model is a designated
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\gls{variable} or function. The goal of the solver is to find the solution that
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maximises or minimises the valuation of the objective}
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description={},
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}
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\newglossaryentry{restart}{
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name={restart},
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description={A restart takes place when a \gls{solver} abandons its current
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search position and start its search from the beginning},
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description={},
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}
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\newglossaryentry{gls-sat}{
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name={boolean satisfiability},
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description={\jip{todo}},
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description={},
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}
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\newglossaryentry{solver}{
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name={solver},
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description={A solver is a dedicated program or algorithm that can be used to
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solve combinatorial problems, or a subset thereof},
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description={},
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}
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\newglossaryentry{parameter}{
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name={problem parameter},
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description={A problem parameter is a constant value that helps define the
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problem. Its value can differ among different problem instances. Its exact value
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must be known when rewriting a constraint model, but is not required when
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compiling a constraint model into a executable program},
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description={},
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}
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\newglossaryentry{propagation}{
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name={constraint propagation},
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description={Constraint propagation is the inference that
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\glspl{variable} can no longer take a certain values, lest they would
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violate a \gls{constraint}},
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description={},
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}
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\newglossaryentry{reification}{
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name={reification},
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description={Instead of enforcing a particular relationship, a reification of
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a constraint predicate will report whether a relationship is present in the
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solution},
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description={},
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}
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% Glossary / Acronyms
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\usepackage[acronym,toc]{glossaries}
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\usepackage[stylemods=bookindex]{glossaries-extra}
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\usepackage{titlecaps}
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\glsdisablehyper{}
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\defglsentryfmt[main]{\ifglsused{\glslabel}{\glsgenentryfmt}{\textit{\glsgenentryfmt}}}
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\usepackage[
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cachedir=listings,
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outputdir=build,
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draft=true,
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]{minted}
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\usemintedstyle{borland}
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@ -116,6 +116,6 @@ following publication:
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\printglossary[nonumberlist,type=\acronymtype]{}
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\renewcommand{\glsnamefont}[1]{\titlecap{#1}}
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\printglossary[nonumberlist]{}
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\printglossary[style=bookindex,title=Index]{}
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\end{document}
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