In the first part of the talk, we reconsider Multiple Quickselect,
which is an algorithm that uses the idea of Quicksort to search for
several order statistics simultaneously. In order to improve the
efficiency of Quicksort, one can use as pivot element in the
partitioning stage the median of randomly chosen elements. Such
a median of
partition can also be applied to Multiple
Quickselect to reduce the number of comparisons. Here we give an
analysis of such Multiple Quickselect variants, that use median of
partition and describe for these algorithms the asymptotic
behaviour of the expected number of required comparisons to find
order statistics in a data set of size
for
and
fixed
.
In the second part of the talk, we consider the size of the induced
spanning subtree of randomly chosen nodes in a random binary
search tree. In our analysis, we use the fact, that the ``spanning
tree size'' is closely related to the number of passes in Multiple
Quickselect (now we mean the ordinary algorithm not the
variants). This parameter, in particular its first two moments were
studied earlier by Panholzer and Prodinger [PP98]. Here we
show also, that this normalized parameter has for fixed
order
statistics a Gaussian limit law. For
this gives the well known
result that the depth of a randomly selected node in a random binary
search tree converges in law to the Normal distribution. For the
spanning tree size, we can also show via generating functions methods,
that for fixed
, the normalized parameter converges in law to the
Normal distribution. The special case
re-proves the recent
result (obtained by Mahmoud and Neininger [MN02] using the
contraction method), that the distribution of distances in random
binary search trees has a Gaussian limit law.