II. Notes on Data Structuring * - Cornell University

end;
end gensupersets;
NOTES ON DATA STRUCTURING 163
untried: v save 2;
trial: - (e)
untried: = save 1
The main program is as follows:
for st: student do
for e in load (st) do
begin examcount (e): + 1; incompat (e): v (load (st) - (e)) end;
while remaining -¢ ( ) do
end;
print timetable
begin s" session;
s: = suitable;
timetable: v {s);
remaining: __ s
Before spending any more efforl~ on developing this program, it would be
advisable to subject it to a critical examination, to ensure that it will be
successful. Now the most obvious reasons why the program might fail are:
(1) The size of the timetable turns out to be unacceptably large; we have
agreed that nothing can be done about this, until we know more about the
data.
(2) The amount of time taken to generate all trials at each step is excessive.
This will be particularly serious when the remainder is still large at the
beginning of the program, and if the untried set remains large on every
recursion of gensupersets. The main way in which the untried set is reduced
is by removing all exams incompatible with the trial. This suggests that we
should always prefer to add first to the trial those exams which have the
largest incompatible sets, so that untried is reduced as quickly as possible.
Among sets equal according to this criterion, the exam with the largest
examcount would be selected first. The exact weighting between these criteria
.
may have to be adjusted later in the light of experience; meanwhile, the
simplest implementation of this policy is to presort the exams in accordance
with the criterion, and implement e from untried by selecting the first
member.
If it turns out that this elementary strategy is insufficient we may have to
artificially curtail the number of iterations of the loop in gensupersets. But
we would probably need some practical experience in order to select a suitable
strategy; and for the time being, let us hope it will not be necessary.
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