Upper Bounds for In-Place Sorting with Minimal Moves

We present the first in-place comparison-based sorting algorithm that sorts an array of $n$ elements using $n\lg n+O(n)$ comparisons with exponentially high probability and always $O(n)$ moves. This matches the information-theoretic lower bound up to an additive linear term despite making only linear moves and working in-place. For the worst-case, we present an algorithm that makes $n\lg n+O(n{\lg }^{(t)}n)$ comparisons and $O(tn)$ data moves, where $t$ is an integer parameter satisfying $2\le t\le {\lg }^{\ast }n-1$ and ${\lg }^{(t)}n$ denotes the $t$-time iterated logarithm, improving over the previous upper bound of $n\lg n+O(n\lg \lg n)$ comparisons and $O(n)$ moves when using constant $t>2$. We thus achieve the ultimate goal of minimal move in-place sorting via randomization whilst narrowing the gap to this goal in the worst-case. This advance primarily relies on a novel ordered set structure that supports searches in an optimal $\lg n+O(1)$ comparisons for $n$ elements.