Improvement with approximate ranking
The new idea
We use an approximate rank for each point chosen by the sorting network.
sign(x) = 1 if x>0, 0 if x=0, and –1 if x<0.
On the other hand, if lies inside [roz-ez,roz+ez], the approximation will not be accurate enough to distinguish the relative position of z and tk, so we must do some extra comparisons to refine roz and reduce ez, until k lies outside the interval. It turns out that most O(nlogn) extra work will be done to refine rank approximations throughout the entire course of the algorithm.
The key structure
Suppose we have a "reference" point at x=r and we know sign(rank(r) - tk). Also suppose that we have partitioned the n lines into tau = E[n/T] groups G1,…,Gtau of size T (maybe Gtau is smaller and T is an integer to be specified) with the property that if k belongs to Gi and l belongs to Gj, and i<j, then yk(r)<=yl(r).
Thus,G1 refers to the lines with the T largest intercepts at x=r, etc…The groups are therefore sorted, but within any particular group, we have no ordering information. Such a structure is called a partition of size T at r. The occasional refinements will divide T by 2 and restore the reference point in time O(n).
A partition of siwe T at r is represented by a permutation p(r,T).
p(r,T) = (p1(r,T),..., pn(r,T)), where (p1(r,T),..., pT(r,T)) are the lines in G1, etc…
Partition p(r,T) is thus an approximation to pi(r). So we define the approximate rank ro(r,T) to be the number of inversions in p(r,T).
Because p(r,T) represents the partition, it can differ from pi(r) only with respect to inversions between pairs of lines within the same group.There are at most "T choose 2" such inversions in a group, there are n/T groups so ro(r,T) differs from rank(k) by less than nT/2. Therefore, for any given value of T, ez <nT/2.
Let T such that, |ro(r,T)-k|<=nT and |ro(r,T/2)-k|>nT/2.
From the last condition, we are able to determine the relative position of r and tk. If these conditions fail for the current value of T, we would halve T and create the partition for the new smaller groups.
Moreover, these two conditions imply the fact that nT/4<|rank(z)-k|<3nT/2.
The flow of control
We now describe the flow of control in the approximation algorithm. The algorithm maintain two reference points : rL<=tk and rR>=tk. We maintain orderings p(rL) and p(rR) and calibrated partition sizes TL and TR for both reference points. The reference with the smaller T is "active".
When the network gives a new query point x=q (weighted median point of n/2 points), if q lies outside [rL, rR], the relative position of q with respect to tk, may be deduced by transivity. We can resolve this point and the relevant zi's of wich q was the weighted median. Otherwise we construct the partition of x=q.
If q< tk, the partition of size T at q is a modification of the partition of size T at rL.
If q> tk, the partition of size T at q is a modification of the partition of size T at rR.
But we do not know the relative ordering of q, an attempt is to modify the partition at r, the active reference point. The attempt will be aborted if |rank(r)-rank(q)| is "large"; in this case, the other reference point becomes active and the partition at this reference point is modified to give the partition at q.
Position q then replaces the appropriate reference point (on the same side of tk), and the reference point with smallest value of T becomes the new active reference point.
We claim that this algorithm has an O(nlogn)-time complexity for any k. (proof by the correctness and the computation of each function, for example, partition, of this algorithm)