Indian National Olympiad in Informatics

Online Programming Contest, 1-2 January 2005

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Advanced Division

Solution to Problem 1: Average

A simple solution that takes time proportional to N*N*N is quite
direct:

-----------------------------------------------------------------

        count=1;
        for i=1 to N   {
             Check if there are j and k such that 
                a_i is the average of a_j and a_k
             if so increment count
        }
        print count

-----------------------------------------------------------------

The check inside the loop will take roughly N*N steps (examine
each pair and see if a_i is the average of the pair).  Thus the
overall time taken is proportional to N*N*N.  This solution would
get 30% of the marks.

However, we can do significantly better. We can find whether a
given element is an average element in roughly N steps instead of
the naive N*N approach. Our first observation leading to this
algorithm is the following: if a_i is the average of a_j and a_k
then one of a_j or a_k must be less than or equal to a_i and the
other must be greater than or equal to a_i.

Suppose the given sequence is sorted in ascending order, i.e.,
a_1 ≤ a_2 ≤ a_3 ... a_N. Let us further assume that the numbers
are distinct (We leave the extension of the solution to the case
where there are repetitions as an exercise.)  Thus a_1 < a_2 <
a_3 ... a_N.

To check if a_i is an average element, we first consider a_{i-1}
and a_{i+1}. If Average(a_{i-1},a_{i+1}) is a_i we are done.
Otherwise, Average(a_{i-1},a_{i+1}) > a_i or
Average(a_{i-1},a_{i+1}) < a_i.

Case 1: Average(a_{i-1},a_{i+1}) > a_i

Then, a_i cannot be the average of a_{i-1} and any other element
--- the average of a_{i-1} and any element a_j with j < i is less
then a_i and the average of a_{i-1} and any element a_j with 
j > i is more than a_i. Thus, we don't need to consider a_{i-1}
anymore.

Case 2: Average(a_{i-1},a_{i+1}) < a_i. 

Then, a_i cannot be the average of a_{i+1} and any other element
--- the average of a_{i+1} and any element a_j with j > i is more
then a_i and the average of a_{i+1} and any element a_j with j < i
is less than or equal to Average(a_{i-1},a_{i+1}) < a_i. Thus, we
don't have to consider a_{i+1} anymore.

This is nice, since we seem to discard a number of pairs after
just one comparison. Can we use this to get a algorithm that takes 
N steps (rather than N*N)?

The more general property, whose proof is identical to the proofs
given above, is the following:

In general, let l < i and u > i.  Suppose Average{a_l,a_u} > a_i.

- (P1) Then, a_i cannot be the average of a_l and any a_j with  
       j ≥ u.

- (P2) Similarly, if Average{a_l,a_u} < a_i then, a_i cannot be the
       average of a_u and any a_j with j ≤ l.

This leads us directly to the efficient algorithm.  Our strategy
is to maintain two indices l and u, such that
  
1) l < i and u > i 
2) a_i is not the average of any pair involving any a_j with l < j < u.

To begin with we initialize l to i-1 and u to i+1. (Recall that
our list has no repetitions and is sorted in ascending order and
so 2 is also satisfied.)

If Average{a_l,a_u} is a_i, we are done.

If Average{a_l,a_u} > a_i then we decrement l by 1.  Clearly 1
still holds. By 2, we already know that for any j with l < j < u,
a_j cannot appear in any pair whose average is a_i. Further, by
(P1) we know that a_i cannot be the average of a_l with any a_j
with j ≥ i. And clearly a_i cannot be the average of a_l with
any j<l. Thus a_i cannot be the average of a_l and any a_j. Thus
decrementing l does not violate 2.

If Average{a_l,a_u} < a_i, we increment u by 1.  A similar
argument will verify that l and u continue to satisfy 1 and 2.

Of course we stop and conclude that a_i is not an average element
if either l reaches 0 or u reaches N+1.

Here is the algorithm:

---------------------------------------------------------------------
       count = 0;
       for i = 1 to N  {
             l = i-1
             u = i+1
             while ((l > 0) and (u ≤ N)) {
                 if (Average(a_l,a_u) = a_i) {
                     count = count + 1
                     break
                 }
                 if (Average(a_l,a_u) < a_i)  
                      u = u+1
                 else 
                      l = l-1
             }
        }
        print count

-----------------------------------------------------------------------

In each iteration of the while loop either l is reduced by 1 or u
is increased by 1. Thus, the while loop can iterate at the most N
times before it finishes. Thus, the entire algorithm takes
roughly N*N steps.

Well, but we assumed that a_1 < a_2 < ... a_N. As long as the
numbers are distinct, we can arrange this by sorting the
array. As we know from earlier problems, this can be done easily
in N*N steps and in N*log(N) steps using clever
algorithms. Either way, the overall time takes time proportional
to N*N (since N*log(N) is much less than N*N).

If the elements are not distinct then, after sorting our array
would look like a_1 ≤ a_2 ≤ ... ≤ a_N.  The above algorithm
can be modified slightly to work in this case too and this is
left as an exercise.  You are strongly encouraged to solve this
exercise as it illustrates a crucial fact --- neat ideas take you
close to the solution, but to solve something completely, you
have to be willing to dirty your hands a bit.
                      
Notice that the only property of "Average" that is used in the
algorithm above is that if x ≤ y and w ≤ z then Average(x,w) ≤
Average(y,w).  This property is called monotonicty.  If we were
asked to compute the number of elements that are sums of two
other elements, or that are sums of squares of 2 other elements
... any such properties that satisfies monotonicity can be solved
as above in time proportional to N*N rather than N*N*N.  (And if
we asked to find one such element we can do it in time N instead
of the N*N that the naive algorithm would take.)



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