<!--
  - Authors:
  -   Christian Schulte <schulte@ps.uni-sb.de>
  -   Gert Smolka <smolka@dfki.de>
  -   Jörg Würtz
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  - Contributors:
  -   Daniel Simon <dansim@ps.uni-sb.de>
  -   Andy Walter <anwalt@ps.uni-sb.de>
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  - Copyright:
  -   Christian Schulte, 1998
  -   Gert Smolka, 1998
  -   Jörg Würtz, 1997
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  -   $Date: 1999/01/21 09:52:52 $ by $Author: schulte $
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<appendix id=appendix.golden-rules><title/Golden Rules/
<p>
We offer the following rules for the design of
efficient constraint programs.

<section class=unnumbered><title/Analyze and Understand your Script/
<p>
The first script for a difficult problem usually
does not show satisfactory performance, even if
you are expert.  To improve it, you need to
analyze and understand the search tree.  The
Explorer is a powerful tool for doing this.  Use
the statistics feature of the Panel to analyse the
performance of your script: how many variables and
propagators have been created?  How often where
the propagators invoked?
</section>

<section class=unnumbered><title/Experiment/
<p>
Once you have analyzed the search tree and
performance of your script, start to experiment
with different models, different distribution
strategies, and propagators for redundant
constraints.
</section>

<section class=unnumbered><title/Have as much Constraint Propagation as Possible/
<p>
More constraint propagation results in smaller
search trees.  Try to design a model that yields
strong propagation.  Try to eliminate symmetries
by imposing canonical orders.  Finally, try to
find redundant constraints that result in
stronger propagation when imposed as
propagators.
</section>

<section class=unnumbered><title/Find a Good Distribution Strategy/
<p>
A good distribution strategy can reduce the size
of the search trees dramatically.  Usually, it's a
good idea to start with a first-fail strategy.
The Grocery Puzzle (see
<ptr to="section.elimination.grocery">) is an example where
domain splitting is much better than trying the
least possible value.  Our script for the Queens
Puzzle (see <ptr to="section.scripts.queens">) can solve
the puzzle even for large ?{N}'s by using a
first-fail distribution strategy that tries the
value in the middle of the domain of the selected
variable first.
</section>

<section class=unnumbered><title/Keep the Number of Variables and Propagators Low/
<p>
The memory consumption of a script depends very
much on the number of propagators and finite
domain variables created.  Models that keep these
numbers low usually lead to more efficient
scripts.  The model for the Queens Problem in
<ptr to="section.scripts.queens"> is particularly
successful in keeping the number of propagators
low.  
<p>
Use the statistics feature of the Panel to find
out how many variables and propagators were
created.
<p>
This rule conflicts with the rule asking for
maximization of constraint propagation.  Extra
propagators for redundant constraints will improve
performance if they reduce significantly either
the size of search tree or the number of
propagation steps (for the latter, see the
Pythagoras Example in
<ptr to="section.propagators.pythagoras">).
</section>

<section class=unnumbered><title/Eliminate Symmetries/
<p>
It is always a good idea to design a model such
that symmetries are avoided as much as possible.
The model for the Queens Puzzle (see
<ptr to="section.scripts.queens">) avoids possible
symmetries by having a minimal number of
variables.  The models for the Grocery and Family
Puzzles (see &nbsp;<ptr to="section.elimination.grocery">
and&nbsp;<ptr to="section.elimination.family">) eliminate symmetries by
imposing a canonical order on the variables by
means of additional constraints.  The model of the
Grocery Puzzle eliminates a subtle symmetry by
stating that the price of the first item must have
a large prime factor in common with the product of
the prices of the items.  The Fraction Puzzle (see
<ptr to="section.propagators.fractions">) eliminates
symmetries by imposing an order on the three
occurring fractions.
</section>

<section class=unnumbered><title/Introduce Propagators for Redundant Constraints/

<p>
Propagators for redundant constraints can often
strengthen a script's propagation.  A redundant
constraint is a constraint that is logically
entailed by the constraints specifying the
problem.  Try to find redundant constraints that
yield nonredundant propagators. The models for the
Fraction and Magic Square puzzles (see
<ptr to="section.propagators.fractions">
and <ptr to="section.propagators.squares">) feature good examples
for nonredundant propagators for redundant
constraints.
</section>

<section class=unnumbered><title/Use Recomputation 
if Memory Consumption is a Problem/

<p>
Scripts which create a large number of variables or propagators or
scripts for which the search tree is very deep might use too much
memory to be feasible. Search engines described in <Ptr.extern
to="ozdoc:system" key="chapter.search"> and the Explorer (see
<Ptr.extern to="ozdoc:explorer">) feature support
for so-called <def/ recomputation/. Recomputation reduces the space
requirements for these problems in that it trades space for time.

</section>
</appendix>

