## Friday, July 02, 2004

### Apple, Wired and FOCS 2004.

Jeff Erickson snarks about Apple's discovery of the wonders of searching as-opposed-to sorting. Just to demonstrate that our community is on the cutting edge of technology trends, I present to you, hot off the presses, from the FOCS 2004 accepted papers list:

No Sorting? Better Searching!
Gianni Franceschini and Roberto Grossi

#### 1 comment:

1. Sorting is commonly meant as the task of arranging keys in increasing or decreasing order (or small variations of this order). Given $n$ keys underlying a total order, the best organization in an array is maintaining them in sorted order. Searching requires $\Theta(\log n)$ comparisons in the worst case, which is optimal.

We demonstrate that this basic fact in data structures does not hold for the general case of multi-dimensional keys, whose comparison cost is proportional to their length. In previous work [STOC94,STOC95,SICOMP01], Andersson, Hagerup, H{\aa}stad and Petersson study the complexity of searching a sorted array of $n$ keys, each of length $k$, arranged in lexicographic (or alphabetic) order for an arbitrary, possibly unbounded, ordered alphabet. They give sophisticated arguments for proving a tight bound in the worst case for this basic data organization, up to a constant factor, obtaining
$\Theta\left(\frac{k \log \log n}{\log \log (4 + \frac{k \log \log n}{\log n})} + k + \log n \right)$
character comparisons (or probes). Note that the bound is $\Theta(\log n)$ when $k=1$, which is the case that is well known in algorithmics.

We describe a novel \emph{permutation} of the $n$ keys that is different from the sorted order, and sorting is just the starting point for describing our preprocessing. When keys are stored according to this unsorted'' order in the array, the complexity of searching drops to
$\Theta\left( k + \log n \right)$
character comparisons (or probes) in the worst case, which is \emph{optimal} among all possible permutations of the $n$ keys in the array, up to a constant factor. Again, the bound is $\Theta(\log n)$ when $k=1$.

Jointly with the aforementioned result of Andersson et al., our finding provably shows that keeping $k$-dimensional keys sorted in an array is \emph{not} the best data organization for searching. This fact was not observable before by just considering $k=O(1)$ as sorting is an optimal organization in this case.

Ciao
-Roberto