501 lines
12 KiB
TeX
501 lines
12 KiB
TeX
% Note: glossary entries for terms that are acronyms should be prefixed 'gls-'
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% so the non-prefixed reference is used to refer to the acronym
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% \newglossaryentry{gls-api}{
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% name={API},
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% description={An Application Programming Interface (API) is a particular set
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% of rules and specifications that a software program can follow to access and
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% make use of the services and resources provided by another particular software
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% program that implements that API},
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% }
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\newglossaryentry{aggregation}{
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name={aggregation},
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description={},
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}
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\newglossaryentry{gls-ampl}{
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name={AMPL:\ A Mathematical Programming Language},
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description={},
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}
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\newglossaryentry{gls-ast}{
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name={Abstract Syntax Tree},
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description={},
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}
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\newglossaryentry{annotation}{
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name={annotation},
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description={},
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}
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\newglossaryentry{array}{
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name={array},
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description={},
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}
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\newglossaryentry{backtrack}{
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name={backtrack},
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description={},
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}
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\newglossaryentry{bnb}{
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name={branch and bound},
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description={},
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}
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\newglossaryentry{bounds-con}{
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name={bounds consistent},
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description={},
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}
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\newglossaryentry{gls-cbls}{
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name={constraint-based local search},
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description={},
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}
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\newglossaryentry{chuffed}{
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name={Chuffed},
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description={},
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}
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\newglossaryentry{comprehension}{
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name={comprehension},
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description={},
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}
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\newglossaryentry{conditional}{
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name={conditional},
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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 formalised rule of a problem. Constraints are generally expressed in term of Boolean logic},
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}
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\newglossaryentry{cml}{
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name={constraint modelling language},
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description={
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A constraint modelling language is a computer language used to define \glspl{model}.
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Low-level constraint modelling languages allow modellers to define \glspl{model} in terms of \gls{native} \glspl{constraint} and \gls{variable} types.
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To create a \gls{slv-mod} the language merely assigns the \glspl{parameter}, (almost) no \gls{rewriting} is required.
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In contrast, high-level constraint modelling languages provide \glspl{global}, allowing modellers to reason at a level different from the targeted \gls{solver}
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},
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}
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\newglossaryentry{constraint-store}{
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name={constraint store},
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description={},
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}
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\newglossaryentry{cplex}{
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name={CPLEX},
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description={},
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}
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\newglossaryentry{gls-chr}{
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name={constraint handling rules},
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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-cp}{
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name={constraint 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={},
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}
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\newglossaryentry{confluence}{
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name={confluence},
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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={},
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}
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\newglossaryentry{gls-cop}{
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name={constraint optimisation problem},
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description={},
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}
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\newglossaryentry{cvar}{
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name={control variable},
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description={
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A control variable is a special form of an \gls{ivar} where the \gls{variable} represent the result of a \gls{reification}
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},
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}
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\newglossaryentry{dec-prb}{
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name={decision problem},
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description={
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A problem which can be defined as making a set of decisions under a certain set of rules.
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Decisions problems can be formalised as \glspl{model}.
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},
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}
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\newglossaryentry{decomp}{
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name={decomposition},
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description={
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A decomposition formulates a \gls{constraint} in terms of other \glspl{constraint} in order to reach \gls{native} \glspl{constraint}.
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Note that the new \glspl{constraint} might represent the same decisions using a different \glspl{variable}, possibly of different types
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},
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}
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\newglossaryentry{del-rew}{
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name={delayed rewriting},
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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={A domain is a set of value that a \gls{variable} can take without violating any \glspl{constraint} in the problem},
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}
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\newglossaryentry{domain-con}{
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name={domain consistent},
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description={},
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}
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\newglossaryentry{essence}{
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name={Essence},
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description={},
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}
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\newglossaryentry{eqsat}{
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name={equisatisfiable},
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description={Two \glspl{model} are equisatisfiable when a bijective function can be defined to map the \glspl{sol} of one \gls{model} onto the \glspl{sol} of the other.},
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}
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\newglossaryentry{fixed}{
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name={fixed},
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description={A \gls{variable} is said to be fixed when there is only a single possible value that it can take},
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}
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\newglossaryentry{fixpoint}{
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name={fix-point},
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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={},
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}
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\newglossaryentry{gecode}{
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name={Gecode},
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description={},
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}
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\newglossaryentry{gls-gbac}{
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name={Generalised Balanced Academic Curriculum},
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description={},
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}
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\newglossaryentry{generator}{
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name={generator},
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description={},
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}
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\newglossaryentry{git}{
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name={Git},
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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={
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A global constraint is a common \gls{constraint} pattern.
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These patterns can generally span any number of \glspl{variable}.
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For example, the well-known global constraint \( \textsc{All\_Different}(\ldots) \) requires all its arguments to take a different value.
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Global constraints are usually not \gls{native} to a \gls{solver}.
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Instead, the \gls{rewriting} process can enforce the global constraint using a \gls{decomp}
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},
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}
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\newglossaryentry{gurobi}{
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name={Gurobi},
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description={},
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}
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\newglossaryentry{half-reif}{
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name={half reification},
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description={},
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}
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\newglossaryentry{indicator-var}{
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name={indicator variable},
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description={},
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}
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\newglossaryentry{ivar}{
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name={introduced variable},
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description={
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An introduced variable is a \gls{variable} that was created in the reformulation of a \gls{decomp}.
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New \gls{variable} are introduced either to redefine an existing \gls{variable} using a different type or to connect newly introduced \glspl{constraint}
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},
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}
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\newglossaryentry{interval}{
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name={interval},
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description={},
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}
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\newglossaryentry{instance}{
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name={instance},
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description={A \gls{model} with assignments for all its \glspl{parameter}},
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}
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\newglossaryentry{knapsack}{
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name={knapsack problem},
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description={},
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}
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\newglossaryentry{let}{
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name={let expression},
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description={},
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}
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\newglossaryentry{gls-lcg}{
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name={lazy clause generation},
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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={},
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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={},
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}
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\newglossaryentry{microzinc}{
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name={Micro\-Zinc},
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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={},
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}
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\newglossaryentry{model}{
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name={constraint model},
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description={
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A constraint model is a formalisation of a \gls{dec-prb} or an \gls{opt-prb}.
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It is defined in terms of formalised decision, \glspl{variable} of different kinds (\eg{} Boolean, integers, or even sets), and \glspl{constraint}, Boolean logic formulas which are forced to hold in any \gls{sol}, and, in case of an \gls{opt-prb}, an \gls{objective}.
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Any external data required to formulate the \glspl{constraint} is said to be the \glspl{parameter}.
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The combination of a constraint model and assignments for its \glspl{parameter} is said to be an \gls{instance} of the constraint model.
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},
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}
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\newglossaryentry{minizinc}{
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name={Mini\-Zinc},
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description={},
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}
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\newglossaryentry{normal-form}{
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name={normal form},
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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={},
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}
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\newglossaryentry{native}{
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name={native},
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description={\glspl{constraint} and \gls{variable} types are said to be native to a \gls{solver} when they can be directly used as input for the \gls{solver}},
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}
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\newglossaryentry{nanozinc}{
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name={Nano\-Zinc},
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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={},
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}
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\newglossaryentry{objective}{
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name={objective function},
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description={An objective function is a function defined over the \glspl{variable} of a \gls{model} to assign a numeric value to the quality of the \gls{sol}.},
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}
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\newglossaryentry{operator}{
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name={operator},
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description={},
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}
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\newglossaryentry{gls-opl}{
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name={OPL:\ The optimisation modelling language},
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description={},
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}
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\newglossaryentry{opt-prb}{
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name={optimisation problem},
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description={
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A decision problem with an additional \gls{objective}.
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It can, likewise, be formalised as a \gls{model}.
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If the problem has multiple \glspl{sol}, then this function can be used to asses the quality on the \gls{sol}.
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For such a problem, an \gls{opt-sol} is a \gls{sol} with the highest quality
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},
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}
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\newglossaryentry{optional}{
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name={optional},
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description={},
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}
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\newglossaryentry{opt-sol}{
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name={optimal solution},
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description={An \gls{sol} in an \gls{opt-prb} for which it has been proven that no other \gls{sol} exist of higher quality},
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}
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\newglossaryentry{restart}{
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name={restart},
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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={},
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}
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\newglossaryentry{search-heuristic}{
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name={search heuristic},
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description={},
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}
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\newglossaryentry{sol}{
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name={solution},
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description={A solution is an assignment for all \glspl{variable} in an \gls{instance} such that no \gls{constraint} is violated},
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}
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\newglossaryentry{solver}{
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name={solver},
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description={
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A solver is a computer program that is designed to solve certain types of \gls{dec-prb} and/or \gls{opt-prb}.
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Given a \gls{slv-mod}, in a specified format, a solver will (eventually) produce a \gls{sol} for it.
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When solving a \gls{opt-prb}, solvers often produce intermediate \glspl{sol} before producing the \gls{opt-sol}
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},
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}
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\newglossaryentry{parameter}{
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name={problem parameter},
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description={
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A problem parameter is immutable data used to define one or more \glspl{constraint}.
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Parameters are part of the external input for a \gls{model}.
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The combination of a \gls{model} and assignment for all its problem parameters is referred to as an \gls{instance} of a \gls{model}
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},
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}
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\newglossaryentry{propagation}{
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name={constraint propagation},
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description={},
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}
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\newglossaryentry{propagator}{
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name={propagator},
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description={},
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}
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\newglossaryentry{qcp-max}{
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name={QCP-max},
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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={A reification is a special form of a \gls{constraint} where, instead of the \gls{constraint} being enforced in the \glspl{sol}, it enforces that a \gls{cvar} represents whether the \gls{constraint} holds or not},
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}
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\newglossaryentry{rewriting}{
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name={rewriting},
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description={
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The process of transforming a \gls{model} \gls{instance} into an \gls{eqsat} \gls{slv-mod} is referred to as rewriting.
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This happens primarily through the application of \glspl{decomp}
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},
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}
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\newglossaryentry{satisfied}{
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name={satisfied},
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description={
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A \gls{constraint} is said to be satisfied when its logical expression is proven to hold.
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Furthermore, a \gls{dec-prb} is said to be satisfied when all its \glspl{constraint} are satisfied
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},
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}
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\newglossaryentry{slv-mod}{
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name={solver model},
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description={
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A solver model is a \gls{instance} of a \gls{model} where all \glspl{constraint} and \gls{variable} types are \gls{native} for the targeted \gls{solver}.
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\glspl{instance} of \glspl{model} containing non-native \glspl{constraint} and/or \gls{variable} types can be transformed into solver models through the process of \gls{rewriting}
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},
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}
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\newglossaryentry{scip}{
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name={SCIP},
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description={},
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}
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\newglossaryentry{term}{
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name={term},
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description={},
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}
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\newglossaryentry{termination}{
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name={termination},
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description={},
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}
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\newglossaryentry{trail}{
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name={trail},
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description={},
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}
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\newglossaryentry{gls-trs}{
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name={term rewriting system},
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description={},
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}
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\newglossaryentry{unify}{
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name={unify},
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description={},
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}
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\newglossaryentry{unsat}{
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name={unsatisfiable},
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description={A problem is unsatisfiable when there exists no possible for the problem},
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}
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\newglossaryentry{variable}{
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name={decision variable},
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description={
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A formalised decision that is yet to be made.
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When searching for a \gls{sol} a decision variable is said to have a certain \gls{domain}, which contains the values that the decision variable might still take.
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If at any point the \gls{domain} is reduced to a single value, then the decision variable is said to be \gls{fixed}.
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If, however, a decision variable has an empty \gls{domain}, then there is no value it can take that is consistent with the \glspl{constraint}
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},
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}
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\newglossaryentry{zinc}{
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name={Zinc},
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description={},
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}
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