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Initial import from LNS paper

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91
assets/bibliography/references.bib
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@ -26,6 +26,77 @@
programming},
}
@inproceedings{michel-2005-comet,
author = {Laurent Michel and Pascal Van Hentenryck},
editor = {Peter van Beek},
title = {The Comet Programming Language and System},
booktitle = {Principles and Practice of Constraint Programming - {CP}
2005, 11th International Conference, {CP} 2005, Sitges, Spain,
October 1-5, 2005, Proceedings},
series = {Lecture Notes in Computer Science},
volume = 3709,
pages = {881--881},
publisher = {Springer},
year = 2005,
url = {https://doi.org/10.1007/11564751_119},
doi = {10.1007/11564751_119},
timestamp = {Tue, 14 May 2019 10:00:45 +0200},
biburl = {https://dblp.org/rec/conf/cp/MichelH05a.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{pisinger-2007-heuristic,
author = {David Pisinger and Stefan Ropke},
title = {A general heuristic for vehicle routing problems},
journal = {Comput. Oper. Res.},
volume = 34,
number = 8,
pages = {2403--2435},
year = 2007,
url = {https://doi.org/10.1016/j.cor.2005.09.012},
doi = {10.1016/j.cor.2005.09.012},
timestamp = {Tue, 18 Feb 2020 13:56:22 +0100},
biburl = {https://dblp.org/rec/journals/cor/PisingerR07.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{rendl-2015-minisearch,
author = {Andrea Rendl and Tias Guns and Peter J. Stuckey and Guido
Tack},
editor = {Gilles Pesant},
title = {MiniSearch: {A} Solver-Independent Meta-Search Language for
MiniZinc},
booktitle = {Principles and Practice of Constraint Programming - 21st
International Conference, {CP} 2015, Cork, Ireland, August 31
- September 4, 2015, Proceedings},
series = {Lecture Notes in Computer Science},
volume = 9255,
pages = {376--392},
publisher = {Springer},
year = 2015,
url = {https://doi.org/10.1007/978-3-319-23219-5_27},
doi = {10.1007/978-3-319-23219-5_27},
timestamp = {Sun, 25 Oct 2020 23:13:15 +0100},
biburl = {https://dblp.org/rec/conf/cp/RendlGST15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{ropke-2006-adaptive,
author = {Stefan Ropke and David Pisinger},
title = {An Adaptive Large Neighborhood Search Heuristic for the
Pickup and Delivery Problem with Time Windows},
journal = {Transp. Sci.},
volume = 40,
number = 4,
pages = {455--472},
year = 2006,
url = {https://doi.org/10.1287/trsc.1050.0135},
doi = {10.1287/trsc.1050.0135},
timestamp = {Tue, 08 Sep 2020 13:28:27 +0200},
biburl = {https://dblp.org/rec/journals/transci/RopkeP06.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@book{silvano-1990-knapsack,
author = {Martello, Silvano and Toth, Paolo},
title = {Knapsack Problems: Algorithms and Computer Implementations},
@ -34,3 +105,23 @@
publisher = {John Wiley \& Sons, Inc.},
address = {USA}
}
@inproceedings{stuckey-2013-functions,
author = {Peter J. Stuckey and Guido Tack},
editor = {Carla P. Gomes and Meinolf Sellmann},
title = {MiniZinc with Functions},
booktitle = {Integration of {AI} and {OR} Techniques in Constraint
Programming for Combinatorial Optimization Problems, 10th
International Conference, {CPAIOR} 2013, Yorktown Heights, NY,
USA, May 18-22, 2013. Proceedings},
series = {Lecture Notes in Computer Science},
volume = 7874,
pages = {268--283},
publisher = {Springer},
year = 2013,
url = {https://doi.org/10.1007/978-3-642-38171-3_18},
doi = {10.1007/978-3-642-38171-3_18},
timestamp = {Tue, 14 May 2019 10:00:47 +0200},
biburl = {https://dblp.org/rec/conf/cpaior/StuckeyT13.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

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@ -17,11 +17,18 @@
name={decision variable},
description={TODO},
}
\newglossaryentry{minizinc}{
name={MiniZinc},
\newglossaryentry{flatzinc}{
name={Flat\-Zinc},
description={TODO},
}
\newglossaryentry{minisearch}{
name={Mini\-Search},
description={TODO},
}
\newglossaryentry{minizinc}{
name={Mini\-Zinc},
description={TODO},
}
\newcommand{\minizinc}{\gls{minizinc}}
\newglossaryentry{solver}{
name={solver},
description={TODO},

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@ -1,4 +1,4 @@
\begin{Verbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{c}{\PYZpc{} Problem parameters}
\PY{k+kt}{enum}\PY{l+s}{ }\PY{n+nv}{TOYS}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{p}{\PYZob{}}\PY{n+nv}{football}\PY{p}{,}\PY{l+s}{ }\PY{n+nv}{tennisball}\PY{p}{,}\PY{l+s}{ }\PY{n+nv}{stuffed\PYZus{}lama}\PY{p}{,}\PY{l+s}{ }\PY{n+nv}{stuffed\PYZus{}elephant}\PY{g+gr}{\PYZcb{}}\PY{p}{;}
\PY{k+kt}{array}\PY{p}{[}\PY{n+nv}{TOYS}\PY{g+gr}{]}\PY{l+s}{ }\PY{k+kt}{of}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{toy\PYZus{}joy}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{p}{[}\PY{l+m}{63}\PY{p}{,}\PY{l+s}{ }\PY{l+m}{12}\PY{p}{,}\PY{l+s}{ }\PY{l+m}{50}\PY{p}{,}\PY{l+s}{ }\PY{l+m}{100}\PY{g+gr}{]}\PY{p}{;}
@ -14,4 +14,4 @@
\PY{c}{\PYZpc{} Goal}
\PY{k}{solve}\PY{l+s}{ }\PY{k}{maximize}\PY{l+s}{ }\PY{n+nv}{total\PYZus{}joy}\PY{p}{;}
\end{Verbatim}
\end{BVerbatim}

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predicate adaptiveUniform(array[int] of var int: x, float: initialDestrRate) =
let { var float: rate; } in
if status() = START then rate = initialDestrRate
elseif status() = UNSAT then rate = min(lastval(rate)-0.02,0.6)
else rate = max(lastval(rate)+0.02,0.95)
endif /\
forall(i in index_set(x))
(if uniform(0.0,1.0) > rate then x[i] = sol(x[i]) else true endif);

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\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{k}{predicate}\PY{l+s}{ }\PY{n+nf}{adaptiveUniform}\PY{p}{(}\PY{k+kt}{array}\PY{p}{[}\PY{k+kt}{int}\PY{g+gr}{]}\PY{l+s}{ }\PY{k+kt}{of}\PY{l+s}{ }\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{x}\PY{p}{,}\PY{l+s}{ }\PY{k+kt}{float}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{initialDestrRate}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{=}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{let}\PY{l+s}{ }\PY{p}{\PYZob{}}\PY{l+s}{ }\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{float}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{rate}\PY{p}{;}\PY{l+s}{ }\PY{g+gr}{\PYZcb{}}\PY{l+s}{ }\PY{o}{in}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{if}\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{n+nf}{status}\PY{p}{(}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nv}{START}\PY{l+s}{ }\PY{k}{then}\PY{l+s}{ }\PY{n+nv}{rate}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nv}{initialDestrRate}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{elseif}\PY{l+s}{ }\PY{n+nf}{status}\PY{p}{(}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nv}{UNSAT}\PY{l+s}{ }\PY{k}{then}\PY{l+s}{ }\PY{n+nv}{rate}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nb}{min}\PY{p}{(}\PY{n+nf}{lastval}\PY{p}{(}\PY{n+nv}{rate}\PY{g+gr}{)}\PY{o}{\PYZhy{}}\PY{l+m}{0.02}\PY{p}{,}\PY{l+m}{0.6}\PY{g+gr}{)}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{else}\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{n+nv}{rate}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nb}{max}\PY{p}{(}\PY{n+nf}{lastval}\PY{p}{(}\PY{n+nv}{rate}\PY{g+gr}{)}\PY{o}{+}\PY{l+m}{0.02}\PY{p}{,}\PY{l+m}{0.95}\PY{g+gr}{)}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{endif}\PY{l+s}{ }\PY{o}{/\PYZbs{}}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{forall}\PY{p}{(}\PY{n+nv}{i}\PY{l+s}{ }\PY{o}{in}\PY{l+s}{ }\PY{n+nb}{index\PYZus{}set}\PY{p}{(}\PY{n+nv}{x}\PY{g+gr}{)}\PY{g+gr}{)}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{p}{(}\PY{k}{if}\PY{l+s}{ }\PY{n+nf}{uniform}\PY{p}{(}\PY{l+m}{0.0}\PY{p}{,}\PY{l+m}{1.0}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{\PYZgt{}}\PY{l+s}{ }\PY{n+nv}{rate}\PY{l+s}{ }\PY{k}{then}\PY{l+s}{ }\PY{n+nv}{x}\PY{p}{[}\PY{n+nv}{i}\PY{g+gr}{]}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nf}{sol}\PY{p}{(}\PY{n+nv}{x}\PY{p}{[}\PY{n+nv}{i}\PY{g+gr}{]}\PY{g+gr}{)}\PY{l+s}{ }\PY{k}{else}\PY{l+s}{ }\PY{l}{true}\PY{l+s}{ }\PY{k}{endif}\PY{g+gr}{)}\PY{p}{;}
\end{BVerbatim}

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@ -0,0 +1,8 @@
array[1..n] of var 1..n: x; % decision variables
var int: cost; % objective function
% ... some constraints defining the problem
% The user-defined LNS strategy
predicate my_lns() = basic_lns(uniformNeighbourhood(x,0.2));
% Solve using my\_lns, restart every 500 nodes, overall timeout 120 seconds
solve ::on_restart("my_lns") ::restart_constant(500) ::timeout(120)
minimize cost;

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@ -0,0 +1,10 @@
\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{k+kt}{array}\PY{p}{[}\PY{l+m}{1}\PY{o}{..}\PY{n+nv}{n}\PY{g+gr}{]}\PY{l+s}{ }\PY{k+kt}{of}\PY{l+s}{ }\PY{k+kt}{var}\PY{l+s}{ }\PY{l+m}{1}\PY{o}{..}\PY{n+nv}{n}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{x}\PY{p}{;}\PY{l+s}{ }\PY{l+s}{ }\PY{c}{\PYZpc{} decision variables}
\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{cost}\PY{p}{;}\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{c}{\PYZpc{} objective function}
\PY{c}{\PYZpc{} ... some constraints defining the problem}
\PY{c}{\PYZpc{} The user-defined LNS strategy}
\PY{k}{predicate}\PY{l+s}{ }\PY{n+nf}{my\PYZus{}lns}\PY{p}{(}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nf}{basic\PYZus{}lns}\PY{p}{(}\PY{n+nf}{uniformNeighbourhood}\PY{p}{(}\PY{n+nv}{x}\PY{p}{,}\PY{l+m}{0.2}\PY{g+gr}{)}\PY{g+gr}{)}\PY{p}{;}
\PY{c}{\PYZpc{} Solve using my\_lns, restart every 500 nodes, overall timeout 120 seconds}
\PY{k}{solve}\PY{l+s}{ }\PY{p}{:}\PY{p}{:}\PY{n+nf}{on\PYZus{}restart}\PY{p}{(}\PY{l+s}{\PYZdq{}}\PY{l+s}{my\PYZus{}lns}\PY{l+s}{\PYZdq{}}\PY{l+s}{) ::restart\PYZus{}constant(500) ::timeout(120)}
\PY{l+s}{ minimize cost;}
\end{BVerbatim}

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var 1..5: s;@ \Vlabel{line:6:status:start}@
constraint status(s);
var bool b1;
constraint int_ne_reif(s,1,b1); % b1 <-> status()!=START @\Vlabel{line:6:status:end}@
var 0.0..1.0: rnd1;@\Vlabel{line:6:x1:start}@
constraint float_uniform(0.0,1.0,rnd1);
var bool: b2;
constraint float_gt_reif(rnd1,0.2,b2);
var bool: b3;
constraint bool_and(b1,b2,b3);
var 1..3: x1;
constraint int_sol(x[1],x1);@\Vlabel{line:6:x1}@
% (status()!=START /\ uniform(0.0,1.0)>0.2) -> x[1]=sol(x[1])
constraint int_eq_imp(x[1],x1,b3); @\Vlabel{line:6:x1:end}@
@...@

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\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{k+kt}{var}\PY{l+s}{ }\PY{l+m}{1}\PY{o}{..}\PY{l+m}{5}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{s}\PY{p}{;}\PY{esc}{ \Vlabel{line:6:status:start}}
\PY{k}{constraint}\PY{l+s}{ }\PY{n+nf}{status}\PY{p}{(}\PY{n+nv}{s}\PY{g+gr}{)}\PY{p}{;}
\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{bool}\PY{l+s}{ }\PY{n+nv}{b1}\PY{p}{;}
\PY{k}{constraint}\PY{l+s}{ }\PY{n+nf}{int\PYZus{}ne\PYZus{}reif}\PY{p}{(}\PY{n+nv}{s}\PY{p}{,}\PY{l+m}{1}\PY{p}{,}\PY{n+nv}{b1}\PY{g+gr}{)}\PY{p}{;}\PY{l+s}{ }\PY{c}{\PYZpc{} b1 <-> status()!=START }\PY{esc}{\Vlabel{line:6:status:end}}
\PY{k+kt}{var}\PY{l+s}{ }\PY{l+m}{0.0}\PY{o}{..}\PY{l+m}{1.0}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{rnd1}\PY{p}{;}\PY{esc}{\Vlabel{line:6:x1:start}}
\PY{k}{constraint}\PY{l+s}{ }\PY{n+nf}{float\PYZus{}uniform}\PY{p}{(}\PY{l+m}{0.0}\PY{p}{,}\PY{l+m}{1.0}\PY{p}{,}\PY{n+nv}{rnd1}\PY{g+gr}{)}\PY{p}{;}
\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{bool}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{b2}\PY{p}{;}
\PY{k}{constraint}\PY{l+s}{ }\PY{n+nf}{float\PYZus{}gt\PYZus{}reif}\PY{p}{(}\PY{n+nv}{rnd1}\PY{p}{,}\PY{l+m}{0.2}\PY{p}{,}\PY{n+nv}{b2}\PY{g+gr}{)}\PY{p}{;}
\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{bool}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{b3}\PY{p}{;}
\PY{k}{constraint}\PY{l+s}{ }\PY{n+nf}{bool\PYZus{}and}\PY{p}{(}\PY{n+nv}{b1}\PY{p}{,}\PY{n+nv}{b2}\PY{p}{,}\PY{n+nv}{b3}\PY{g+gr}{)}\PY{p}{;}
\PY{k+kt}{var}\PY{l+s}{ }\PY{l+m}{1}\PY{o}{..}\PY{l+m}{3}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{x1}\PY{p}{;}
\PY{k}{constraint}\PY{l+s}{ }\PY{n+nf}{int\PYZus{}sol}\PY{p}{(}\PY{n+nv}{x}\PY{p}{[}\PY{l+m}{1}\PY{g+gr}{]}\PY{p}{,}\PY{n+nv}{x1}\PY{g+gr}{)}\PY{p}{;}\PY{esc}{\Vlabel{line:6:x1}}
\PY{c}{\PYZpc{} (status()!=START /\ uniform(0.0,1.0)>0.2) -> x[1]=sol(x[1])}
\PY{k}{constraint}\PY{l+s}{ }\PY{n+nf}{int\PYZus{}eq\PYZus{}imp}\PY{p}{(}\PY{n+nv}{x}\PY{p}{[}\PY{l+m}{1}\PY{g+gr}{]}\PY{p}{,}\PY{n+nv}{x1}\PY{p}{,}\PY{n+nv}{b3}\PY{g+gr}{)}\PY{p}{;}\PY{l+s}{ }\PY{esc}{\Vlabel{line:6:x1:end}}
\PY{esc}{...}
\end{BVerbatim}

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predicate hill_climbing() =
if status()=START then true
else _objective < sol(_objective) endif;

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@ -0,0 +1,5 @@
\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{k}{predicate}\PY{l+s}{ }\PY{n+nf}{hill\PYZus{}climbing}\PY{p}{(}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{=}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{if}\PY{l+s}{ }\PY{n+nf}{status}\PY{p}{(}\PY{g+gr}{)}\PY{o}{=}\PY{n+nv}{START}\PY{l+s}{ }\PY{k}{then}\PY{l+s}{ }\PY{l}{true}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{else}\PY{l+s}{ }\PY{l+s}{\PYZus{}}\PY{l+s}{o}\PY{l+s}{b}\PY{l+s}{j}\PY{l+s}{e}\PY{l+s}{c}\PY{l+s}{t}\PY{l+s}{i}\PY{l+s}{v}\PY{l+s}{e}\PY{l+s}{ }\PY{o}{\PYZlt{}}\PY{l+s}{ }\PY{n+nf}{sol}\PY{p}{(}\PY{l+s}{\PYZus{}}\PY{l+s}{o}\PY{l+s}{b}\PY{l+s}{j}\PY{l+s}{e}\PY{l+s}{c}\PY{l+s}{t}\PY{l+s}{i}\PY{l+s}{v}\PY{l+s}{e}\PY{g+gr}{)}\PY{l+s}{ }\PY{k}{endif}\PY{p}{;}
\end{BVerbatim}

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function ann: lns(var int: obj, array[int] of var int: vars,
int: iterations, float: destrRate, int: exploreTime) =
repeat (i in 1..iterations) ( scope(
if has_sol() then post(uniformNeighbourhood(vars,destrRate))
else true endif /\
time_limit(exploreTime, minimize_bab(obj)) /\
commit() /\ print()
) /\ post(obj < sol(obj)) );

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\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{k}{function}\PY{l+s}{ }\PY{k+kt}{ann}\PY{p}{:}\PY{l+s}{ }\PY{n+nf}{lns}\PY{p}{(}\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{obj}\PY{p}{,}\PY{l+s}{ }\PY{k+kt}{array}\PY{p}{[}\PY{k+kt}{int}\PY{g+gr}{]}\PY{l+s}{ }\PY{k+kt}{of}\PY{l+s}{ }\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{vars}\PY{p}{,}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{iterations}\PY{p}{,}\PY{l+s}{ }\PY{k+kt}{float}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{destrRate}\PY{p}{,}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{exploreTime}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{=}
\PY{l+s}{ }\PY{l+s}{ }\PY{n+nv}{repeat}\PY{l+s}{ }\PY{p}{(}\PY{n+nv}{i}\PY{l+s}{ }\PY{o}{in}\PY{l+s}{ }\PY{l+m}{1}\PY{o}{..}\PY{n+nv}{iterations}\PY{g+gr}{)}\PY{l+s}{ }\PY{p}{(}\PY{l+s}{ }\PY{n+nf}{scope}\PY{p}{(}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{k}{if}\PY{l+s}{ }\PY{n+nf}{has\PYZus{}sol}\PY{p}{(}\PY{g+gr}{)}\PY{l+s}{ }\PY{k}{then}\PY{l+s}{ }\PY{n+nf}{post}\PY{p}{(}\PY{n+nf}{uniformNeighbourhood}\PY{p}{(}\PY{n+nv}{vars}\PY{p}{,}\PY{n+nv}{destrRate}\PY{g+gr}{)}\PY{g+gr}{)}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{k}{else}\PY{l+s}{ }\PY{l}{true}\PY{l+s}{ }\PY{k}{endif}\PY{l+s}{ }\PY{o}{/\PYZbs{}}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{n+nf}{time\PYZus{}limit}\PY{p}{(}\PY{n+nv}{exploreTime}\PY{p}{,}\PY{l+s}{ }\PY{n+nf}{minimize\PYZus{}bab}\PY{p}{(}\PY{n+nv}{obj}\PY{g+gr}{)}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{/\PYZbs{}}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{n+nf}{commit}\PY{p}{(}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{/\PYZbs{}}\PY{l+s}{ }\PY{n+nf}{print}\PY{p}{(}\PY{g+gr}{)}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{/\PYZbs{}}\PY{l+s}{ }\PY{n+nf}{post}\PY{p}{(}\PY{n+nv}{obj}\PY{l+s}{ }\PY{o}{\PYZlt{}}\PY{l+s}{ }\PY{n+nf}{sol}\PY{p}{(}\PY{n+nv}{obj}\PY{g+gr}{)}\PY{g+gr}{)}\PY{l+s}{ }\PY{g+gr}{)}\PY{p}{;}
\end{BVerbatim}

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@ -0,0 +1,3 @@
predicate uniformNeighbourhood(array[int] of var int: x, float: destrRate) =
forall(i in index_set(x))
(if uniform(0.0,1.0) > destrRate then x[i] = sol(x[i]) else true endif);

5
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@ -0,0 +1,5 @@
\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{k}{predicate}\PY{l+s}{ }\PY{n+nf}{uniformNeighbourhood}\PY{p}{(}\PY{k+kt}{array}\PY{p}{[}\PY{k+kt}{int}\PY{g+gr}{]}\PY{l+s}{ }\PY{k+kt}{of}\PY{l+s}{ }\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{x}\PY{p}{,}\PY{l+s}{ }\PY{k+kt}{float}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{destrRate}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{=}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{forall}\PY{p}{(}\PY{n+nv}{i}\PY{l+s}{ }\PY{o}{in}\PY{l+s}{ }\PY{n+nb}{index\PYZus{}set}\PY{p}{(}\PY{n+nv}{x}\PY{g+gr}{)}\PY{g+gr}{)}
\PY{l+s}{ }\PY{l+s}{ }\PY{p}{(}\PY{k}{if}\PY{l+s}{ }\PY{n+nf}{uniform}\PY{p}{(}\PY{l+m}{0.0}\PY{p}{,}\PY{l+m}{1.0}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{\PYZgt{}}\PY{l+s}{ }\PY{n+nv}{destrRate}\PY{l+s}{ }\PY{k}{then}\PY{l+s}{ }\PY{n+nv}{x}\PY{p}{[}\PY{n+nv}{i}\PY{g+gr}{]}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nf}{sol}\PY{p}{(}\PY{n+nv}{x}\PY{p}{[}\PY{n+nv}{i}\PY{g+gr}{]}\PY{g+gr}{)}\PY{l+s}{ }\PY{k}{else}\PY{l+s}{ }\PY{l}{true}\PY{l+s}{ }\PY{k}{endif}\PY{g+gr}{)}\PY{p}{;}
\end{BVerbatim}

18
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@ -0,0 +1,18 @@
% post predicate "pred" whenever the solver restarts
annotation on_restart(string: pred);
% restart after fixed number of nodes
annotation restart_constant(int: nodes);
% restart with scaled Luby sequence
annotation restart_luby(int: scale);
% restart with scaled geometric sequence ($scale*base^n$ in the $n$-th iteration)
annotation restart_geometric(float: base, int: scale);
% restart with linear sequence ($scale*n$ in the $n$-th iteration)
annotation restart_linear(int: scale);
% restart on each solution
annotation restart_on_solution;
% restart without branch-and-bound constraints on the objective
annotation restart_without_objective;
% overall time limit for search
annotation timeout(int: seconds);
% overall limit on number of restarts
annotation restart_limit(int: n_restarts);

20
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@ -0,0 +1,20 @@
\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{c}{\PYZpc{} post predicate "pred" whenever the solver restarts}
\PY{k}{annotation}\PY{l+s}{ }\PY{n+nf}{on\PYZus{}restart}\PY{p}{(}\PY{k+kt}{string}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{pred}\PY{g+gr}{)}\PY{p}{;}
\PY{c}{\PYZpc{} restart after fixed number of nodes}
\PY{k}{annotation}\PY{l+s}{ }\PY{n+nf}{restart\PYZus{}constant}\PY{p}{(}\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{nodes}\PY{g+gr}{)}\PY{p}{;}
\PY{c}{\PYZpc{} restart with scaled Luby sequence}
\PY{k}{annotation}\PY{l+s}{ }\PY{n+nf}{restart\PYZus{}luby}\PY{p}{(}\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{scale}\PY{g+gr}{)}\PY{p}{;}
\PY{c}{\PYZpc{} restart with scaled geometric sequence ($scale*base^n$ in the $n$-th iteration)}
\PY{k}{annotation}\PY{l+s}{ }\PY{n+nf}{restart\PYZus{}geometric}\PY{p}{(}\PY{k+kt}{float}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{base}\PY{p}{,}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{scale}\PY{g+gr}{)}\PY{p}{;}
\PY{c}{\PYZpc{} restart with linear sequence ($scale*n$ in the $n$-th iteration)}
\PY{k}{annotation}\PY{l+s}{ }\PY{n+nf}{restart\PYZus{}linear}\PY{p}{(}\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{scale}\PY{g+gr}{)}\PY{p}{;}
\PY{c}{\PYZpc{} restart on each solution}
\PY{k}{annotation}\PY{l+s}{ }\PY{n+nv}{restart\PYZus{}on\PYZus{}solution}\PY{p}{;}
\PY{c}{\PYZpc{} restart without branch-and-bound constraints on the objective}
\PY{k}{annotation}\PY{l+s}{ }\PY{n+nv}{restart\PYZus{}without\PYZus{}objective}\PY{p}{;}
\PY{c}{\PYZpc{} overall time limit for search}
\PY{k}{annotation}\PY{l+s}{ }\PY{n+nf}{timeout}\PY{p}{(}\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{seconds}\PY{g+gr}{)}\PY{p}{;}
\PY{c}{\PYZpc{} overall limit on number of restarts}
\PY{k}{annotation}\PY{l+s}{ }\PY{n+nf}{restart\PYZus{}limit}\PY{p}{(}\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{n\PYZus{}restarts}\PY{g+gr}{)}\PY{p}{;}
\end{BVerbatim}

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@ -0,0 +1,7 @@
predicate round_robin(array[int] of var bool: nbhs) =
let { int: N = length(nbhs);
var -1..N-1: select; % Neighbourhood selection
} in if status()=START then select= -1
else select= (lastval(select) + 1) mod N
endif /\
forall(i in 1..N) (select=i-1 -> nbhs[i]);@\Vlabel{line:6:roundrobin:post}@

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@ -0,0 +1,9 @@
\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{k}{predicate}\PY{l+s}{ }\PY{n+nf}{round\PYZus{}robin}\PY{p}{(}\PY{k+kt}{array}\PY{p}{[}\PY{k+kt}{int}\PY{g+gr}{]}\PY{l+s}{ }\PY{k+kt}{of}\PY{l+s}{ }\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{bool}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{nbhs}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{=}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{k}{let}\PY{l+s}{ }\PY{p}{\PYZob{}}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{N}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nb}{length}\PY{p}{(}\PY{n+nv}{nbhs}\PY{g+gr}{)}\PY{p}{;}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{k+kt}{var}\PY{l+s}{ }\PY{o}{\PYZhy{}}\PY{l+m}{1}\PY{o}{..}\PY{n+nv}{N}\PY{o}{\PYZhy{}}\PY{l+m}{1}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{select}\PY{p}{;}\PY{l+s}{ }\PY{c}{\PYZpc{} Neighbourhood selection}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{g+gr}{\PYZcb{}}\PY{l+s}{ }\PY{o}{in}\PY{l+s}{ }\PY{l+s}{ }\PY{k}{if}\PY{l+s}{ }\PY{n+nf}{status}\PY{p}{(}\PY{g+gr}{)}\PY{o}{=}\PY{n+nv}{START}\PY{l+s}{ }\PY{k}{then}\PY{l+s}{ }\PY{n+nv}{select}\PY{o}{=}\PY{l+s}{ }\PY{o}{\PYZhy{}}\PY{l+m}{1}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{k}{else}\PY{l+s}{ }\PY{n+nv}{select}\PY{o}{=}\PY{l+s}{ }\PY{p}{(}\PY{n+nf}{lastval}\PY{p}{(}\PY{n+nv}{select}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{+}\PY{l+s}{ }\PY{l+m}{1}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{mod}\PY{l+s}{ }\PY{n+nv}{N}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{k}{endif}\PY{l+s}{ }\PY{o}{/\PYZbs{}}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{k}{forall}\PY{p}{(}\PY{n+nv}{i}\PY{l+s}{ }\PY{o}{in}\PY{l+s}{ }\PY{l+m}{1}\PY{o}{..}\PY{n+nv}{N}\PY{g+gr}{)}\PY{l+s}{ }\PY{p}{(}\PY{n+nv}{select}\PY{o}{=}\PY{n+nv}{i}\PY{o}{\PYZhy{}}\PY{l+m}{1}\PY{l+s}{ }\PY{o}{\PYZhy{}\PYZgt{}}\PY{l+s}{ }\PY{n+nv}{nbhs}\PY{p}{[}\PY{n+nv}{i}\PY{g+gr}{]}\PY{g+gr}{)}\PY{p}{;}\PY{esc}{\Vlabel{line:6:roundrobin:post}}
\end{BVerbatim}

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@ -0,0 +1,7 @@
predicate simulated_annealing(float: initTemp, float: coolingRate) =
let { var float: temp; } in
if status()=START then temp = initTemp
else
temp = lastval(temp)*(1-coolingRate) /\ % cool down
_objective < sol(_objective) - ceil(log(uniform(0.0,1.0)) * temp)
endif;

9
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@ -0,0 +1,9 @@
\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{k}{predicate}\PY{l+s}{ }\PY{n+nf}{simulated\PYZus{}annealing}\PY{p}{(}\PY{k+kt}{float}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{initTemp}\PY{p}{,}\PY{l+s}{ }\PY{k+kt}{float}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{coolingRate}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{=}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{let}\PY{l+s}{ }\PY{p}{\PYZob{}}\PY{l+s}{ }\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{float}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{temp}\PY{p}{;}\PY{l+s}{ }\PY{g+gr}{\PYZcb{}}\PY{l+s}{ }\PY{o}{in}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{if}\PY{l+s}{ }\PY{n+nf}{status}\PY{p}{(}\PY{g+gr}{)}\PY{o}{=}\PY{n+nv}{START}\PY{l+s}{ }\PY{k}{then}\PY{l+s}{ }\PY{n+nv}{temp}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nv}{initTemp}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{else}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{n+nv}{temp}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{n+nf}{lastval}\PY{p}{(}\PY{n+nv}{temp}\PY{g+gr}{)}\PY{o}{*}\PY{p}{(}\PY{l+m}{1}\PY{o}{\PYZhy{}}\PY{n+nv}{coolingRate}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{/\PYZbs{}}\PY{l+s}{ }\PY{c}{\PYZpc{} cool down}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{\PYZus{}}\PY{l+s}{o}\PY{l+s}{b}\PY{l+s}{j}\PY{l+s}{e}\PY{l+s}{c}\PY{l+s}{t}\PY{l+s}{i}\PY{l+s}{v}\PY{l+s}{e}\PY{l+s}{ }\PY{o}{\PYZlt{}}\PY{l+s}{ }\PY{n+nf}{sol}\PY{p}{(}\PY{l+s}{\PYZus{}}\PY{l+s}{o}\PY{l+s}{b}\PY{l+s}{j}\PY{l+s}{e}\PY{l+s}{c}\PY{l+s}{t}\PY{l+s}{i}\PY{l+s}{v}\PY{l+s}{e}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{\PYZhy{}}\PY{l+s}{ }\PY{n+nb}{ceil}\PY{p}{(}\PY{n+nb}{log}\PY{p}{(}\PY{n+nf}{uniform}\PY{p}{(}\PY{l+m}{0.0}\PY{p}{,}\PY{l+m}{1.0}\PY{g+gr}{)}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{*}\PY{l+s}{ }\PY{n+nv}{temp}\PY{g+gr}{)}
\PY{l+s}{ }\PY{l+s}{ }\PY{k}{endif}\PY{p}{;}
\end{BVerbatim}

5
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@ -0,0 +1,5 @@
% Report the status of the solver (before restarting).
enum STATUS = {START, UNKNOWN, UNSAT, SAT, OPT} @\label{ann:enum_status}@
function STATUS: status(); @\label{ann:status}@
% Provide access to the last assigned value of variable x.
function int: lastval(var int: x);

7
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@ -0,0 +1,7 @@
\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{c}{\PYZpc{} Report the status of the solver (before restarting).}
\PY{k+kt}{enum}\PY{l+s}{ }\PY{n+nv}{STATUS}\PY{l+s}{ }\PY{o}{=}\PY{l+s}{ }\PY{p}{\PYZob{}}\PY{n+nv}{START}\PY{p}{,}\PY{l+s}{ }\PY{n+nv}{UNKNOWN}\PY{p}{,}\PY{l+s}{ }\PY{n+nv}{UNSAT}\PY{p}{,}\PY{l+s}{ }\PY{n+nv}{SAT}\PY{p}{,}\PY{l+s}{ }\PY{n+nv}{OPT}\PY{g+gr}{\PYZcb{}}\PY{l+s}{ }\PY{esc}{\label{ann:enum_status}}
\PY{k}{function}\PY{l+s}{ }\PY{n+nv}{STATUS}\PY{p}{:}\PY{l+s}{ }\PY{n+nf}{status}\PY{p}{(}\PY{g+gr}{)}\PY{p}{;}\PY{l+s}{ }\PY{esc}{\label{ann:status}}
\PY{c}{\PYZpc{} Provide access to the last assigned value of variable x.}
\PY{k}{function}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nf}{lastval}\PY{p}{(}\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{x}\PY{g+gr}{)}\PY{p}{;}
\end{BVerbatim}

5
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@ -0,0 +1,5 @@
predicate status(var int: stat); @\Vlabel{line:6:status}@
function var STATUS: status() =
let { var STATUS: stat;
constraint status(stat);
} in stat;

7
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@ -0,0 +1,7 @@
\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{k}{predicate}\PY{l+s}{ }\PY{n+nf}{status}\PY{p}{(}\PY{k+kt}{var}\PY{l+s}{ }\PY{k+kt}{int}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{stat}\PY{g+gr}{)}\PY{p}{;}\PY{l+s}{ }\PY{esc}{\Vlabel{line:6:status}}
\PY{k}{function}\PY{l+s}{ }\PY{k+kt}{var}\PY{l+s}{ }\PY{n+nv}{STATUS}\PY{p}{:}\PY{l+s}{ }\PY{n+nf}{status}\PY{p}{(}\PY{g+gr}{)}\PY{l+s}{ }\PY{o}{=}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{k}{let}\PY{l+s}{ }\PY{p}{\PYZob{}}\PY{l+s}{ }\PY{k+kt}{var}\PY{l+s}{ }\PY{n+nv}{STATUS}\PY{p}{:}\PY{l+s}{ }\PY{n+nv}{stat}\PY{p}{;}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{k}{constraint}\PY{l+s}{ }\PY{n+nf}{status}\PY{p}{(}\PY{n+nv}{stat}\PY{g+gr}{)}\PY{p}{;}
\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{l+s}{ }\PY{g+gr}{\PYZcb{}}\PY{l+s}{ }\PY{o}{in}\PY{l+s}{ }\PY{n+nv}{stat}\PY{p}{;}
\end{BVerbatim}

3
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@ -0,0 +1,3 @@
predicate assign_random(array[int] of var int: X, int: R) =
forall (i in index_set(X))
(if uniform(1,100) < R then X[i] = sol(X[i]) else true endif);

3
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@ -0,0 +1,3 @@
predicate basic_LNS() =
(status() != START) -> nbh(X);

19
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@ -0,0 +1,19 @@
% A search annotation that supplies a function to be executed on restart
ann: on_restart(string: pred); @\Vlabel{ann:on_restart}@
% An annotation for FlatZinc that accesses solver state
ann: solver_state;
% The 'status' function reports the status of the solver (before restarting).
enum STATUS = {START, UNKNOWN, UNSAT, SAT, OPT} @\Vlabel{ann:enum_status}@
function var STATUS: status(); @\Vlabel{ann:status}@
% The 'sol' functions provides access to solution values of model variables.
% The sol functions are meaningful when the solver status is not START.
function var bool: sol(var bool: x); @\Vlabel{ann:sol1}@
function var float: sol(var float: x); @\Vlabel{ann:sol2}@
function var int: sol(var int: x); @\Vlabel{ann:sol3}@
function var set of int: sol(var set of int: x); @\Vlabel{ann:sol4}@
% The 'lastval' functions provides access to the last assigned value of variables.
% The lastval functions are meaningful when the solver status is not START.
function var bool: lastval(var bool: x);
function var float: lastval(var float: x);
function var int: lastval(var int: x);
function var set of int: lastval(var set of int: x);

37
assets/packages.tex
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@ -1,3 +1,4 @@
\hfuzz=1.5pt
\usepackage{csquotes}
\usepackage[australian]{babel}
\usepackage{hyperref}
@ -36,9 +37,7 @@ BoldItalicFont=*-BoldItalic,
]
%% Mathmatical font
\usepackage{unicode-math}
\setmathfont[
Scale=1.4
]{GFSNeohellenicMath.otf}
\setmathfont{GFSNeohellenicMath.otf}
% References
\usepackage[
@ -48,6 +47,7 @@ style=apa,
% Glossary / Acronyms
\usepackage[acronym,toc]{glossaries}
\glsdisablehyper
\defglsentryfmt[main]{\ifglsused{\glslabel}{\glsgenentryfmt}{\textit{\glsgenentryfmt}}}
\makeglossaries{}
@ -55,23 +55,30 @@ style=apa,
\usepackage{fancyvrb}
\usepackage{color}
\input{assets/pygments_header.tex}
\newcommand{\highlightfile}[1]{\input{#1tex}}
\DeclareNewTOC[
type=program,
type=listing,
float,
name=Program,
name=Listing,
counterwithin=chapter,
atbegin={%
\centering
\scriptsize
}
]{program}
\crefname{program}{program}{programs}
\DeclareNewTOC[
type=model,
float,
name=Model,
counterwithin=chapter,
atbegin={%
\scriptsize
\crefname{listing}{listing}{listings}
\newcommand{\Vlabel}[1]{\label[line]{#1}\hypertarget{#1}{}}
\newcommand{\lref}[1]{\hyperlink{#1}{\FancyVerbLineautorefname~\ref*{#1}}}
\newcommand{\lrefrange}[2]{\FancyVerbLineautorefname{}s~\hyperlink{#1}{\ref*{#1}}--\hyperlink{#2}{\ref*{#2}}}
\newcommand{\Lrefrange}[2]{Lines~\hyperlink{#1}{\ref*{#1}}--\hyperlink{#2}{\ref*{#2}}}
% TODO: What am I doing with this?
\newcommand*\justify{%
\fontdimen2\font=0.4em% interword space
\fontdimen3\font=0.2em% interword stretch
\fontdimen4\font=0.1em% interword shrink
\fontdimen7\font=0.1em% extra space
\hyphenchar\font=`\-% allowing hyphenation
}
]{model}
\crefname{model}{model}{models}
\newcommand{\mzninline}[1]{\texttt{\small\justify\detokenize{#1}}}

4
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@ -1,4 +1,4 @@
\begin{Verbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\begin{BVerbatim}[commandchars=\\\{\},numbers=left,firstnumber=1,stepnumber=1,codes={\catcode`\$=3\catcode`\^=7\catcode`\_=8}]
\PY{n}{toys\PYZus{}joy} \PY{o}{=} \PY{p}{[}\PY{l+m+mi}{63}\PY{p}{,} \PY{l+m+mi}{12}\PY{p}{,} \PY{l+m+mi}{50}\PY{p}{,} \PY{l+m+mi}{100}\PY{p}{]}
\PY{n}{toys\PYZus{}space} \PY{o}{=} \PY{p}{[}\PY{l+m+mi}{32}\PY{p}{,} \PY{l+m+mi}{8}\PY{p}{,} \PY{l+m+mi}{16}\PY{p}{,} \PY{l+m+mi}{40}\PY{p}{]}
\PY{n}{space\PYZus{}left} \PY{o}{=} \PY{l+m+mi}{64}
@ -27,4 +27,4 @@
\PY{n}{table}\PY{p}{[}\PY{n}{i}\PY{p}{]}\PY{p}{[}\PY{n}{j}\PY{p}{]} \PY{o}{=} \PY{n}{table}\PY{p}{[}\PY{n}{i} \PY{o}{\PYZhy{}} \PY{l+m+mi}{1}\PY{p}{]}\PY{p}{[}\PY{n}{j}\PY{p}{]}
\PY{n}{optimal\PYZus{}joy} \PY{o}{=} \PY{n}{table}\PY{p}{[}\PY{n}{num\PYZus{}toys}\PY{p}{]}\PY{p}{[}\PY{n}{space\PYZus{}left}\PY{p}{]}
\end{Verbatim}
\end{BVerbatim}

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assets/shorthands.tex Normal file
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@ -0,0 +1,4 @@
\newcommand{\eg}{e.g.}
\newcommand{\flatzinc}{\gls{flatzinc}}
\newcommand{\minisearch}{\gls{minisearch}}
\newcommand{\minizinc}{\gls{minizinc}}

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chapters/2_background.tex
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@ -22,33 +22,33 @@ amount of space left in the car, so we cannot bring all the toys. Since Audrey
gets enjoys playing with some toys more than others, we can now try and pick the
toys that bring Audrey the most amount of joy, but still fit in the car.
\begin{program}[ht]
\input{assets/py/2_dyn_knapsack.pytex}
\caption{\label{prog:dyn-knapsack} A Python program that solves a 0-1 knapsack
\begin{listing}[ht]
\highlightfile{assets/py/2_dyn_knapsack.py}
\caption{\label{lst:2-dyn-knapsack} A Python program that solves a 0-1 knapsack
problem using dynamic programming}
\end{program}
\end{listing}
A well educated reader in optimisation problems might immediately recognise that
this is a variation on the widely known \textit{knapsack problem}, more
specifically a \textit{0-1 knapsack problem}
\autocite[13--67]{silvano-1990-knapsack}. A commonly used solution to this
problem is based on dynamic programming. An implementation of this approach is
shown in \cref{prog:dyn-knapsack}. In a naive recursive approach we would try
shown in \cref{lst:2-dyn-knapsack}. In a naive recursive approach we would try
all different combinations of toys to find the combination that will give the
most joy, but using a dynamic programming approach this exponential behaviour
(on the number of toys) can be avoided.
\begin{model}[ht]
\input{assets/mzn/2_knapsack.mzntex}
\caption{\label{model:knapsack} A \minizinc\ model describing a 0-1 knapsack
\begin{listing}[ht]
\highlightfile{assets/mzn/2_knapsack.mzn}
\caption{\label{lst:2-mzn-knapsack} A \minizinc\ model describing a 0-1 knapsack
problem}
\end{model}
\end{listing}
A constraint model offers a different view of the problem. Instead of specifying
the manner in which we can find the solution, we give a concise description of
the problem in terms of what we already know, the \glspl{problem-parameter},
what we wish to know, the \glspl{decision-variable}, and the relationships that
should exists between them, the \glspl{constraint}. \Cref{model:knapsack} shows
should exists between them, the \glspl{constraint}. \Cref{lst:2-mzn-knapsack} shows
a \minizinc\ model of the knapsack problem, where the different elements of the
constraint model are separated. Although a constraint model does not contain any
instructions to find a suitable solutions, these models can generally be given

528
chapters/6_incremental.tex
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@ -1,3 +1,529 @@
%************************************************
\chapter{Incremental Solving}\label{ch:incremental}
%************************************************
\section{Modelling of Neighbourhoods and Meta-heuristics}
\label{section:2-modelling-nbhs}
%
% Start with a brief review of most common neighbourhoods and then explain:
% \begin{itemize}
% \item Random built in with integers
% \begin{itemize}
% \item Explain the built in
% \item Give an example of use (use model)
% \item Limitations if any
% \end{itemize}
% \item Solution based one
% \begin{itemize}
% \item Explain the built in
% \item Give an example of use (use model)
% \item Limitations if any
% \end{itemize}
% \end{itemize}
% End with future work for other built ins (hint which ones would be useful).
Most LNS literature discusses neighbourhoods in terms of ``destroying'' part of
a solution that is later repaired. However, from a declarative modelling point
of view, it is more natural to see neighbourhoods as adding new constraints and
variables that need to be applied to the base model, \eg forcing variables to
take the same value as in the previous solution.
This section introduces a \minizinc\ extension that enables modellers to define
neighbourhoods using the $\mathit{nbh(a)}$ approach described above. This
extension is based on the constructs introduced in
\minisearch\~\autocite{rendl-2015-minisearch}, as summarised below.
\subsection{LNS in \glsentrytext{minisearch}}
\minisearch\ introduced a \minizinc\ extension that enables modellers to express
meta-searches inside a \minizinc\ model. A meta-search in \minisearch\ typically
solves a given \minizinc\ model, performs some calculations on the solution, adds
new constraints and then solves again.
An LNS definition in \minisearch\ consists of two parts. The first part is a
declarative definition of a neighbourhood as a \minizinc\ predicate that posts
the constraints that should be added with respect to a previous solution. This
makes use of the \minisearch\ function: \mzninline{function int: sol(var int:
x)}, which returns the value that variable \mzninline{x} was assigned to in
the previous solution (similar functions are defined for Boolean, float and set
variables). In addition, a neighbourhood predicate will typically make use of
the random number generators available in the \minizinc\ standard library.
\Cref{lst:6-lns-minisearch-pred} shows a simple random neighbourhood. For each
decision variable \mzninline{x[i]}, it draws a random number from a uniform
distribution and, if it exceeds threshold \mzninline{destrRate}, posts
constraints forcing \mzninline{x[i]} to take the same value as in the previous
solution. For example, \mzninline{uniformNeighbourhood(x, 0.2)} would result in
each variable in the array \mzninline{x} having a 20\% chance of being
unconstrained, and an 80\% chance of being assigned to the value it had in the
previous solution.
\begin{listing}
\highlightfile{assets/mzn/6_lns_minisearch_pred.mzn}
\caption{\label{lst:6-lns-minisearch-pred} A simple random LNS predicate
implemented in \minisearch{}}
\end{listing}
\begin{listing}
\highlightfile{assets/mzn/6_lns_minisearch.mzn}
\caption{\label{lst:6-lns-minisearch} A simple LNS metaheuristic implemented
in \minisearch{}}
\end{listing}
The second part of a \minisearch\ LNS is the meta-search itself. The most basic
example is that of function \mzninline{lns} in \cref{lst:6-lns-minisearch}. It
performs a fixed number of iterations, each invoking the neighbourhood predicate
\mzninline{uniformNeighbourhood} in a fresh scope (so that the constraints only
affect the current loop iteration). It then searches for a solution
(\mzninline{minimize_bab}) with a given timeout, and if the search does return a
new solution, it commits to that solution (so that it becomes available to the
\mzninline{sol} function in subsequent iterations). The \texttt{lns} function
also posts the constraint \mzninline{obj < sol(obj)}, ensuring the objective
value in the next iteration is strictly better than that of the current
solution.
\paragraph{Limitations of the \minisearch\ approach.}
Although \minisearch\ enables the modeller to express \emph{neighbourhoods} in a
declarative way, the definition of the \emph{meta-search} is rather unintuitive
and difficult to debug, leading to unwieldy code for defining simple restarting
strategies. Furthermore, the \minisearch\ implementation requires either a close
integration of the backend solver into the \minisearch\ system, or it drives the
solver through the regular text-file based \flatzinc\ interface, leading to a
significant communication overhead.
To address these two issues for LNS, we propose to keep modelling neighbourhoods
as predicates, but define a small number of additional \minizinc\ built-in
annotations and functions that (a) allow us to express important aspects of the
meta-search in a more convenient way, and (b) enable a simple compilation scheme
that requires no additional communication with and only small, simple extensions
of the backend solver.
The approach we follow here is therefore to \textbf{extend \flatzinc}, such that
the definition of neighbourhoods can be communicated to the solver together with
the problem instance. This maintains the loose coupling of \minizinc\ and
solver, while avoiding the costly communication and cold-starting of the
black-box approach.
\subsection{Restart annotations}
Instead of the complex \minisearch\ definitions, we propose to add support for
simple meta-searches that are purely based on the notion of \emph{restarts}. A
restart happens when a solver abandons its current search efforts, returns to
the root node of the search tree, and begins a new exploration. Many CP solvers
already provide support for controlling their restarting behaviour, e.g.\ they
can periodically restart after a certain number of nodes, or restart for every
solution. Typically, solvers also support posting additional constraints upon
restarting (e.g Comet~\autocite{michel-2005-comet}) that are only valid for the
particular restart (i.e., they are ``retracted'' for the next restart).
In its simplest form, we can therefore implement LNS by specifying a
neighbourhood predicate, and annotating the \mzninline{solve} item to indicate
the predicate should be invoked upon each restart:
\mzninline{solve ::on_restart(myNeighbourhood) minimize cost;}
Note that \minizinc\ currently does not support passing functions or predicates
as arguments. Calling the predicate, as in
\mzninline{::on_restart(myNeighbourhood())}, would not have the correct
semantics, since the predicate needs to be called for \emph{each} restart. As a
workaround, we currently pass the name of the predicate to be called for each
restart as a string (see the definition of the new \mzninline{on_restart}
annotation in \cref{lst:6-restart-ann}).
The second component of our LNS definition is the \emph{restarting strategy},
defining how much effort the solver should put into each neighbourhood (i.e.,
restart), and when to stop the overall search.
We propose adding new search annotations to \minizinc\ to control this behaviour
(see \cref{lst:6-restart-ann}). The \mzninline{restart_on_solution} annotation
tells the solver to restart immediately for each solution, rather than looking
for the best one in each restart, while \mzninline{restart_without_objective}
tells it not to add branch-and-bound constraints on the objective. The other
\mzninline{restart_X} annotations define different strategies for restarting the
search when no solution is found. The \mzninline{timeout} annotation gives an
overall time limit for the search, whereas \mzninline{restart_limit} stops the
search after a fixed number of restarts.
\begin{listing}[t]
\highlightfile{assets/mzn/6_restart_ann.mzn}
\caption{\label{lst:6-restart-ann} New annotations to control the restarting
behaviour}
\end{listing}
\subsection{Neighbourhood selection}
It is often beneficial to use several neighbourhood definitions for a problem.
Different neighbourhoods may be able to improve different aspects of a solution,
at different phases of the search. Adaptive LNS \autocite{ropke-2006-adaptive,
pisinger-2007-heuristic}, which keeps track of the neighbourhoods that led to
improvements and favours them for future iterations, is the prime example for
this approach. A simpler scheme may apply several neighbourhoods in a
round-robin fashion.
In \minisearch\, adaptive or round-robin approaches can be implemented using
\emph{state variables}, which support destructive update (overwriting the value
they store). In this way, the \minisearch\ strategy can store values to be used
in later iterations. We use the \emph{solver state} instead, i.e., normal
decision variables, and define two simple built-in functions to access the
solver state \emph{of the previous restart}. This approach is sufficient for
expressing neighbourhood selection strategies, and its implementation is much
simpler.
\paragraph{State access and initialisation}
The state access functions are defined in \cref{lst:6-state-access}. Function
\mzninline{status} returns the status of the previous restart, namely:
\mzninline{START} (there has been no restart yet); \mzninline{UNSAT} (the
restart failed); \mzninline{SAT} (the restart found a solution); \mzninline{OPT}
(the restart found and proved an optimal solution); and \mzninline{UNKNOWN} (the
restart did not fail or find a solution). Function \mzninline{lastval} (which,
like \mzninline{sol}, has versions for all basic variable types) allows
modellers to access the last value assigned to a variable (the value is
undefined if \mzninline{status()=START}).
\begin{listing}[t]
\highlightfile{assets/mzn/6_state_access.mzn}
\caption{\label{lst:6-state-access} Functions for accessing previous solver
states}
\end{listing}
In order to be able to initialise the variables used for state access, we
reinterpret \mzninline{on_restart} so that the predicate is also called for the
initial search (i.e., before the first ``real'' restart) with the same
semantics, that is, any constraint posted by the predicate will be retracted for
the next restart.
\paragraph{Parametric neighbourhood selection predicates}
We define standard neighbourhood selection strategies as predicates that are
parametric over the neighbourhoods they should apply. For example, since
\mzninline{on_restart} now also includes the initial search, we can define a
strategy \mzninline{basic_lns} that applies a neighbourhood only if the current
status is not \mzninline{START}:
\mzninline{predicate basic_lns(var bool: nbh) = (status()!=START -> nbh);}
In order to use this predicate with the \mzninline{on_restart} annotation, we
cannot simply pass \mzninline{basic_lns(uniformNeighbourhood(x,0.2))}. First of
all, calling \mzninline{uniformNeighbourhood} like that would result in a
\emph{single} evaluation of the predicate, since \minizinc\ employs a
call-by-value evaluation strategy. Furthermore, the \mzninline{on_restart}
annotation only accepts the name of a nullary predicate. Therefore, users have
to define their overall strategy in a new predicate. \Cref{lst:6-basic-complete}
shows a complete example of a basic LNS model.
\begin{listing}[t]
\highlightfile{assets/mzn/6_basic_complete.mzn}
\caption{\label{lst:6-basic-complete} Complete LNS example}
\end{listing}
We can also define round-robin and adaptive strategies using these primitives.
%\paragraph{Round-robin LNS}
\Cref{lst:6-round-robin} defines a round-robin LNS meta-heuristic, which cycles
through a list of \mzninline{N} neighbourhoods \mzninline{nbhs}. To do this, it
uses the decision variable \mzninline{select}. In the initialisation phase
(\mzninline{status()=START}), \mzninline{select} is set to \mzninline{-1}, which
means none of the neighbourhoods is activated. In any following restart,
\mzninline{select} is incremented modulo \mzninline{N}, by accessing the last
value assigned in a previous restart (\mzninline{lastval(select)}). This will
activate a different neighbourhood for each restart
(\lref{line:6:roundrobin:post}).
\begin{listing}[t]
\highlightfile{assets/mzn/6_round_robin.mzn}
\caption{\label{lst:6-round-robin} A predicate providing the round robin
meta-heuristic}
\end{listing}
%\paragraph{Adaptive LNS}
For adaptive LNS, a simple strategy is to change the size of the neighbourhood
depending on whether the previous size was successful or not.
\Cref{lst:6-adaptive} shows an adaptive version of the
\mzninline{uniformNeighbourhood} that increases the number of free variables
when the previous restart failed, and decreases it when it succeeded, within the
bounds $[0.6,0.95]$.
\begin{listing}[t]
\highlightfile{assets/mzn/6_adaptive.mzn}
\caption{\label{lst:6-adaptive} A simple adaptive neighbourhood}
\end{listing}
\subsection{Meta-heuristics}
The LNS strategies we have seen so far rely on the default behaviour of
\minizinc\ solvers to use branch-and-bound for optimisation: when a new solution
is found, the solver adds a constraint to the remainder of the search to only
accept better solutions, as defined by the objective function in the
\mzninline{minimize} or \mzninline{maximize} clause of the \mzninline{solve}
item. When combined with restarts and LNS, this is equivalent to a simple
hill-climbing meta-heuristic.
We can use the constructs introduced above to implement alternative
meta-heuristics such as simulated annealing. In particular, we use
\mzninline{restart_without_objective} to tell the solver not to add the
branch-and-bound constraint on restart. It will still use the declared objective
to decide whether a new solution is the globally best one seen so far, and only
output those (to maintain the convention of \minizinc\ solvers that the last
solution printed at any point in time is the currently best known one).
%
With \mzninline{restart_without_objective}, the restart predicate is now
responsible for constraining the objective function. Note that a simple
hill-climbing (for minimisation) can still be defined easily in this context as:
{
\centering
\scriptsize
\highlightfile{assets/mzn/6_hill_climbing.mzn}
}
It takes advantage of the fact that the declared objective function is available
through the built-in variable \mzninline{_objective}.
%
A simulated annealing strategy is also easy to
express:
{
\centering
\scriptsize
\highlightfile{assets/mzn/6_simulated_annealing.mzn}
}
\section{Compilation of Neighbourhoods} \label{section:compilation}
The neighbourhoods defined in the previous section can be executed with
\minisearch\ by adding support for the \mzninline{status} and
\mzninline{lastval} built-in functions, and by defining the main restart loop.
The \minisearch{} evaluator will then call a solver to produce a solution, and
evaluate the neighbourhood predicate, incrementally producing new \flatzinc\ to
be added to the next round of solving.
%
While this is a viable approach, our goal is to keep the compiler and solver
separate, by embedding the entire LNS specification into the \flatzinc\ that is
passed to the solver.
%
This section introduces such a compilation approach. It only requires simple
modifications of the \minizinc\ compiler, and the compiled \flatzinc\ can be
executed by standard CP solvers with a small set of simple extensions.
\subsection{Compilation overview}
The neighbourhood definitions from the previous section have an important
property that makes them easy to compile to standard \flatzinc: they are defined
in terms of standard \minizinc\ expressions, with the exception of a few new
built-in functions. When the neighbourhood predicates are evaluated in the
\minisearch\ way, the \minisearch\ runtime implements those built-in functions,
computing the correct value whenever a predicate is evaluated.
%
Instead, the compilation scheme presented below uses a limited form of
\emph{partial evaluation}: parameters known at compile time will be fully
evaluated; those only known during the solving, such as the result of a call to
any of the new functions (\mzninline{sol}, \mzninline{status}, etc.), are
replaced by decision variables. This essentially \textbf{turns the new built-in
functions into constraints} that have to be supported by the target solver.
The neighbourhood predicate can then be added as a constraint to the model. The
evaluation is performed by hijacking the solver's own capabilities: It will
automatically perform the evaluation of the new functions by propagating the new
constraints.
To compile an LNS specification to standard \flatzinc, the \minizinc\ compiler
performs four simple steps:
\begin{enumerate}
\item Replace the annotation \mzninline{::on_restart("X")} with a call to
predicate \mzninline{X}.
\item Inside predicate \mzninline{X} and any other predicate called
recursively from \mzninline{X}: treat any call to built-in functions
\mzninline{sol}, \mzninline{status}, and \mzninline{lastval} as
returning a \mzninline{var} instead of a \mzninline{par} value; and
rename calls to random functions, e.g., \mzninline{uniform} to
\mzninline{uniform_nbh}, in order to distinguish them from their
standard library versions.
\item Convert any expression containing a call from step 2 to \mzninline{var}
to ensure the functions are compiled as constraints, rather than
statically evaluated by the \minizinc\ compiler.
\item Compile the resulting model using an extension of the \minizinc\
standard library that provides declarations for these built-in
functions, as defined below.
\end{enumerate}
These transformations will not change the code of many neighbourhood
definitions, since the built-in functions are often used in positions that
accept both parameters and variables. For example, the
\mzninline{uniformNeighbourhood} predicate from \cref{lst:6-lns-minisearch-pred}
uses \mzninline{uniform(0.0,1.0)} in an \mzninline{if} expression, and
\mzninline{sol(x[i])} in an equality constraint. Both expressions can be
translated to \flatzinc\ when the functions return a \mzninline{var}.
\subsection{Compiling the new built-ins}
We can compile models that contain the new built-ins by extending the \minizinc\
standard library as follows.
\paragraph{\mzninline{status}}
\Cref{lst:6-status} shows the definition of the \mzninline{status} function. It
simply replaces the functional form by a predicate \mzninline{status} (declared
in \lref{line:6:status}), which constrains its local variable argument
\mzninline{stat} to take the status value.
\begin{listing}[t]
\highlightfile{assets/mzn/6_status.mzn}
\caption{\label{lst:6-status} MiniZinc definition of the \mzninline{status} function}
\end{listing}
\paragraph{\mzninline{sol} and \mzninline{lastval}}
Since \mzninline{sol} is overloaded for different variable types and \flatzinc\
does not support overloading, we produce type-specific built-ins for every type
of solver variable (\mzninline{int_sol(x, xi)}, \mzninline{bool_sol(x, xi)},
etc.). The resolving of the \mzninline{sol} function into these specific
built-ins is done using an overloaded definition like the one shown
in~\Cref{lst:6-int-sol} for integer variables. If the value of the variable in
question becomes known at compile time, we use that value instead. Otherwise, we
replace the function call with a type specific \mzninline{int_sol} predicate,
which is the constraint that will be executed by the solver.
%
\begin{listing}[t]
\centering
% \begin{mzn}
% predicate int_sol(var int: x, var int: xi);
% function int: sol(var int: x) = if is_fixed(x) then fix(x)
% else let { var lb(x)..ub(x): xi;
% constraint int_sol(x,xi);
% } in xi;
% endif;
% \end{mzn}
\caption{\label{lst:6-int-sol} MiniZinc definition of the \mzninline{sol}
function for integer variables}
\end{listing}
%
To improve the compilation of the model further, we use the declared bounds of
the argument (\mzninline{lb(x)..ub(x)}) to constrain the variable returned by
\mzninline{sol}. This bounds information is important for the compiler to be
able to generate the most efficient \flatzinc\ code for expressions involving
\mzninline{sol}. The compilation of \mzninline{lastval} is similar to that for
\mzninline{sol}.
\paragraph{Random number functions}
Calls to the random number functions have been renamed by appending
\texttt{\_nbh}, so that the compiler does not simply evaluate them statically.
The definition of these new functions follows the same pattern as for
\mzninline{sol}, \mzninline{status}, and \mzninline{lastval}. The MiniZinc
definition of the \mzninline{uniform_nbh} function is shown in
\Cref{lst:6-int-rnd}.%
\footnote{Random number functions need to be marked as \mzninline{::impure} for
the compiler not to apply Common Subexpression Elimination
(CSE)~\autocite{stuckey-2013-functions} if they are called multiple times with
the same arguments.}%
Note that the function accepts variable arguments \mzninline{l} and
\mzninline{u}, so that it can be used in combination with other functions, such
as \mzninline{sol}.
\begin{listing}[t]
\centering
% \begin{mzn}
% predicate float_uniform(var float:l, var float: u, var float: r);
% function var float: uniform_nbh(var float: l, var float: u) :: impure =
% let { var lb(l)..ub(u): rnd;
% constraint float_uniform(l,u,rnd):
% } in rnd;
% \end{mzn}
\caption{\label{lst:6-int-rnd} MiniZinc definition of the
\mzninline{uniform_nbh} function for floats}
\end{listing}
\subsection{Solver support for LNS \glsentrytext{flatzinc}}
We will now show the minimal extensions required from a solver to interpret the
new \flatzinc\ constraints and, consequently, to execute LNS definitions
expressed in \minizinc.
First, the solver needs to parse and support the restart annotations
of~\cref{lst:6-restart-ann}. Many solvers already support all this
functionality. Second, the solver needs to be able to parse the new constraints
\mzninline{status}, and all versions of \mzninline{sol}, \mzninline{lastval},
and random number functions like \mzninline{float_uniform}. In addition, for the
new constraints the solver needs to:
\begin{itemize}
\item \mzninline{status(s)}: record the status of the previous restart, and
fix \mzninline{s} to the recorded status.
\item \mzninline{sol(x,sx)} (variants): constrain \mzninline{sx} to be equal
to the value of \mzninline{x} in the incumbent solution. If there is no
incumbent solution, it has no effect.
\item \mzninline{lastval(x,lx)} (variants): constrain \mzninline{lx} to take
the last value assigned to \mzninline{x} during search. If no value was
ever assigned, it has no effect. Note that many solvers (in particular
SAT and LCG solvers) already track \mzninline{lastval} for their
variables for use in search. To support LNS a solver must at least track
the \emph{lastval} of each of the variables involved in such a
constraint. This is straightforward by using the \mzninline{lastval}
propagator itself. It wakes up whenever the first argument is fixed, and
updates the last value (a non-backtrackable value).
\item random number functions: fix their variable argument to a random number
in the appropriate probability distribution.
\end{itemize}
Importantly, these constraints need to be propagated in a way that their effects
can be undone for the next restart. Typically, this means the solver must not
propagate these constraints in the root node of the search.
Modifying a solver to support this functionality is straightforward if it
already has a mechanism for posting constraints during restarts. We have
implemented these extensions for both Gecode (110 new lines of code) and Chuffed
(126 new lines of code).
For example, consider the model from \cref{lst:6-basic-complete} again.
\Cref{lst:6-flat-pred} shows a part of the \flatzinc\ that arises from compiling
\mzninline{basic_lns(uniformNeighbourhood(x, 0.2))}, assuming that
\mzninline{index_set(x) = 1..n}.
\Lrefrange{line:6:status:start}{line:6:status:end} define a Boolean variable
\mzninline{b1} that is true iff the status is not \mzninline{START}. The second
block of code (\lrefrange{line:6:x1:start}{line:6:x1:end}) represents the
decomposition of the expression
\mzninline{(status() != START /\ uniform(0.0,1.0) > 0.2) -> x[1] = sol(x[1])}
%
which is the result of merging the implication from the \mzninline{basic_lns}
predicate with the \mzninline{if} expression from
\mzninline{uniformNeighbourhood}. The code first introduces and constrains a
variable for the random number, then adds two Boolean variables: \mzninline{b2}
is constrained to be true iff the random number is greater than 0.2; while
\mzninline{b3} is constrained to be the conjunction \mzninline{status()!=START
/\ uniform(0.0,1.0)>0.2}. \lref{line:6:x1} constrains \mzninline{x1} to be the
value of \mzninline{x[1]} in the previous solution. Finally, the half-reified
constraint in \lref{line:6:x1:end} implements \mzninline{b3 -> x[1]=sol(x[1])}.
We have omitted the similar code generated for \mzninline{x[2]} to
\mzninline{x[n]}. Note that the \flatzinc\ shown here has been simplified for
presentation.
\begin{listing}[t]
\highlightfile{assets/mzn/6_basic_complete_transformed.mzn}
\caption{\label{lst:6-flat-pred} \flatzinc\ that results from compiling \\
\mzninline{basic_lns(uniformNeighbourhood(x,0.2))}.}
\end{listing}
The first time the solver is invoked, it sets \mzninline{s} to 1
(\mzninline{START}). Propagation will fix \mzninline{b1} to \mzninline{false}
and \mzninline{b3} to \mzninline{false}. Therefore, the implication in
\lref{line:6:x1:end} is not activated, leaving \mzninline{x[1]} unconstrained.
The neighbourhood constraints are effectively switched off.
When the solver restarts, all of the special propagators are re-executed.
Now \mzninline{s} is not 1, and \mzninline{b1} will be set to
\mzninline{true}. The \mzninline{float_random} propagator assigns
\mzninline{rnd1} a new random value and, depending on whether it is greater
than \mzninline{0.2}, the Boolean variables \mzninline{b2}, and consequently
\mzninline{b3} will be assigned. If it is \mzninline{true}, the constraint
in line \lref{line:6:x1:end} will become active and assign \mzninline{x[1]}
to its value in the previous solution.
Furthermore, it is not strictly necessary to guard \mzninline{int_uniform}
against being invoked before \mzninline{status()!=START}, since the
\mzninline{sol} constraints will simply not propagate anything in case no
solution has been recorded yet, but we use this simple example to illustrate
how these Boolean conditions are compiled and evaluated.

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dekker_thesis.tex
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@ -9,6 +9,7 @@ DIV=calc,
\input{assets/packages}
\input{assets/layout}
\input{assets/shorthands}
% Bibliography preamble
\addbibresource{assets/bibliography/references.bib}