Add acronyms to glossary entries

assets/acronyms.tex

@@ -14,7 +14,7 @@
 
 \newacronym[see={[Glossary:]{gls-cp}}]{cp}{CP\glsadd{gls-cp}}{\emph{Constraint Programming}}
 
-\newacronym[see={[Glossary:]{gls-cse}}]{cse}{CSE\glsadd{gls-cse}}{Common Sub-expression Elimination}
+\newacronym[see={[Glossary:]{gls-cse}}]{cse}{CSE\glsadd{gls-cse}}{\emph{Common Sub-expression Elimination}}
 
 \newacronym{cnf}{CNF\glsadd{cnf}}{\emph{Conjunctive Normal Form}}
 

assets/glossary.tex

@@ -29,7 +29,7 @@
 }
 
 \newglossaryentry{gls-ast}{
-  name={Abstract Syntax Tree},
+  name={Abstract Syntax Tree (AST)},
   description={A tree structure representing the syntactic structure of a piece of computer language. These structures are often used in a \gls{compiler} or \gls{interpreter}.},
 }
 
@@ -97,12 +97,12 @@
 }
 
 \newglossaryentry{gls-cbc}{
-  name={COIN-OR Branch-and-Cut},
+  name={COIN-OR Branch-and-Cut (CBC)},
   description={A well-known open source \gls{mip} \gls{solver} \autocite{forrest-2020-cbc}.},
 }
 
 \newglossaryentry{gls-cbls}{
-  name={constraint-based local search},
+  name={constraint-based local search (CBLS)},
   description={A form of local search using \gls{constraint} violations as its \gls{objective}. The search method estimate the amount a \gls{constraint} is \gls{violated} in terms of a numeric value, the objective of the search is to minimize this value. Generally \constraints{} can also offer ``search steps'' that can be taken that do not introduce new violations.},
 }
 
@@ -152,17 +152,17 @@
 }
 
 \newglossaryentry{gls-chr}{
-  name={constraint handling rules},
+  name={constraint handling rules (CHR)},
   description={A non-deterministic declarative rule based programming language to maintain or improve a constraint store. Constraint handling rules are often used as an extension of \gls{clp}. See \cref{sub:back-chr}.},
 }
 
 \newglossaryentry{gls-clp}{
-  name={constraint logic programming},
+  name={constraint logic programming (CLP)},
   description={An extension of logic programming to include the concepts of \gls{cp}. A constraint logic program manipulates a \gls{constraint} store to find relevant variants where all \glspl{constraint} contained in the store are \gls{satisfied}. See \cref{subsec:back-clp}.},
 }
 
 \newglossaryentry{gls-cp}{
-  name={constraint programming},
+  name={constraint programming (CP)},
   description={
 		A general technique to find \glspl{sol} to \glspl{instance} of \glspl{model}.
 		A constraint programming solver interleaves \gls{propagation} with making \glspl{search-decision} to reduce the \gls{search-space}.
@@ -171,7 +171,7 @@
 }
 
 \newglossaryentry{gls-cse}{
-  name={common sub-expression elimination},
+  name={common sub-expression elimination (CSE)},
   description={A technique stemming from programming languages to avoid duplicate computation. In \glspl{cml} this technique is used to avoid the creation of duplicate \glspl{variable} and \glspl{constraint}. See \cref{subsec:back-cse}.},
 }
 
@@ -344,12 +344,12 @@
 }
 
 \newglossaryentry{gls-lcg}{
-  name={lazy clause generation},
+  name={lazy clause generation (LCG)},
   description={Types of \gls{solver} that extend \gls{cp} solving with \gls{sat} capabilities. Lazy clause generation \glspl{solver} lazily translate a \gls{cp} model for a \gls{sat} backend. As such, they maintain the pruning ability of a \gls{cp} solver while gaining the ability of \gls{sat} solvers to explain any failures in the search.},
 }
 
 \newglossaryentry{gls-lns}{
-  name={large neighbourhood search},
+  name={large neighbourhood search (LNS)},
   description={
 		A \gls{meta-optimization} strategy that repeatedly reduces the \gls{search-space} by applying different \glspl{neighbourhood} often based on a previous \gls{sol}. Large neighbourhood search is a well-known method to quickly improve a \gls{sol}. Large neighbourhood search is not guaranteed to find the \gls{opt-sol} and if it does, then it will be unable to prove that it did.
 	}
@@ -402,12 +402,12 @@
 }
 
 \newglossaryentry{gls-maxsat}{
-  name={Maximum Satisfiability},
+  name={Maximum Satisfiability (MaxSAT)},
   description={An extension of the \gls{gls-sat} problem class into an \gls{opt-prb}. A \gls{gls-sat} problem in \gls{gls-cnf} is extended with weights for each clause. An \gls{opt-sol} of a problem \instance{} maximizes the weights of the \gls{satisfied} clauses.},
 }
 
 \newglossaryentry{gls-mip}{
-  name={mixed integer programming},
+  name={mixed integer programming (MIP)},
   description={
     A solving technique that tries to find the \gls{opt-sol} for a \cmodel{} containing a mixture of Integer and floating point \glspl{variable} subject to \glspl{constraint} in the form of linear (in-)equations.
     Mixed integer programming is extensively studied and there are many successful \glspl{solver} dedicated to solving mixed integer programs.
@@ -493,7 +493,7 @@
 }
 
 \newglossaryentry{gls-sat}{
-  name={Boolean satisfiability},
+  name={Boolean satisfiability (SAT)},
   description={
     A problem class that tries to find a valid \gls{assignment} for a set of Boolean \glspl{variable} subject to a logical formula.
     Boolean satisfiability is extensively studied and there are many \glspl{solver} dedicated to solving this problem class.