In-place algorithms for sorting problems
Authors: Pasanen Tomi 
Published: October 2004

An algorithm is said to operate in-place if it uses only a
constant amount of extra memory for storing local variables besides
the memory reserved for the input elements. In other words, the
size of the extra memory does not grow as the number of input
elements, <i>n</i>, gets larger, but it is bounded by a
constant. An algorithm reorders the input elements stably if the
original relative order of equal elements is retained.
In this thesis, we devise in-place algorithms for sorting and
related problems. We measure the efficiency of the algorithms by
calculating the number of element comparisons and element moves
performed in the worst case in the following. The amount of index
manipulation operations is closely related to these quantities, so
it is omitted in our calculations. When no precise figures are
needed, we denote the sum of all operations by a general expression
"time". The thesis consists of five separate articles, the main
contributions of which are described below.
We construct algorithms for stable partitioning and stable
selection which are the first linear-time algorithms being both
stable and in-place concurrently. Moreover, we define problems
stable unpartitioning and restoring selection and devise
linear-time algorithms for these problems. The algorithm for stable
un-partitioning is in-place while that for restoring selection uses
O(<i>n</i>) extra bits. By using these algorithms as
subroutines we construct an adaption of Quicksort that sorts a
multiset stably in
<i>O</i>(&Sigma;<sup><i>k</i></sup><sub><i>i</i>
= 1</sub> <i>m<sub>i</sub></i>
log(<i>n/m<sub>i</sub></i>)) time where
<i>m<sub>i</sub></i> is the multiplicity of
ith distinct element for <i>i</i> = 1,..,
<i>k</i>. This is the first in-place algorithm that
sorts a multiset stably in asymptotically optimal time.
We present in-place algorithms for unstable and stable merging.
The algorithms are asymptotically more efficient than earlier ones:
the number of moves is 3(<i>n +
m</i>)+<i>o(m)</i> for the unstable algorithm,
5<i>n</i>+12<i>m</i>+<i>o</i>(<i>m</i>)
for the stable algorithm, and the number of comparisons at most
<i>m</i>(<i>t</i> + 1) +
<i>n</i>/2<sup>t</sup> +
<i>o</i>(<i>m</i>) comparisons where m
&le; <i>n</i> and <i>t</i> =
[log(<i>n/m</i>)]. The previous best results were
1.125(<i>n + m</i>) +
<i>o</i>(<i>n</i>) comparisons and
5(<i>n + in</i>) + <i>o(n)</i> moves for
unstable merging, and 16.5(<i>n + in</i>) +
<i>o(n)</i> moves for stable merging.
Finally, we devise two in-place algorithms for sorting. Both
algorithms are adaptions of Mergesort. The first performs
<i>n</i> log<sub>2</sub>
<i>n</i> + O(<i>n</i>) comparisons and
&#949; <i>n</i> loge <i>n</i> +
O(<i>n</i> log log <i>n</i>) moves for any
fixed 0 &lt; &#949; &le; 2. Our experiments show that
this algorithm performs well in practice The second requires
<i>n</i> loge <i>n</i> +
O(<i>n</i>) comparisons and fewer than
O(<i>n</i> log <i>n</i>/log log
<i>n</i>) moves. This is the first in-place sorting
algorithm that performs <i>o</i>(<i>n</i>
log <i>n</i>) moves in the worst case while
guaranteeing O(<i>n</i> log <i>n</i>)
comparisons.