Gianni Franceschini

Radix sorting with no extra space

It iswell known that n integers in the range [1, nc] canbe sorted in O(n) time in the RAM model using radix sorting. More generally, integers in any range [1, U] can be sorted in O(n√log log n) time [5]. However, these algorithms use O(n) words of extra memory. Is this necessary? We present a simple, stable, integer sorting algorithm for words of size O(log n), which works in O(n) time and uses only O(1) words of extra memory on a RAM model. This is the integer sorting case most useful in practice. We extend this result with same bounds to the case when the keys are read-only, which is of theoretical interest. Another interesting question is the case of arbitrary c. Here we present a black-box transformation from any RAM sorting algorithm to a sorting algorithm which uses only O(1) extra space and has the same running time. This settles the complexity of in-place sorting in terms of the complexity of sorting.

Optimal worst-case operations for implicit cache-oblivious search trees

We close an open issue on dictionaries dating back to the sixthies. An array of n keys can be sorted so that searching takes O(log n) time. Alternatively, it can be organized as a heap so that inserting and deleting keys take O(log n) time. We show that these bounds can be simultaneously achieved in the worst case for searching and updating by suitably maintaining a permutation of the n keys in the array. The resulting data structure is called implicit as it uses just O(1) extra memory cells beside the n cells for the array. The data structure is also cache-oblivious, attaining O(logB n) block transfers in the worst case for any (unknown) value of the block size B, without wasting any single cell of memory at any level of the memory hierarchy.

An in-place sorting with O ( n log n ) comparisons and O ( n ) moves

We present the first in-place algorithm for sorting an array of size n that performs, in the worst case, at most O(nlog n) element comparisons and O(n) element transports.This solves a long-standing open problem, stated explicitly, for example, in Munro and Raman [1992], of whether there exists a sorting algorithm that matches the asymptotic lower bounds on all computational resources simultaneously.

In-place suffix sorting

Given string T = T[1,..., n], the suffix sorting problem is to lexicographically sort the suffixes T[i,..., n] for all i. This problem is central to the construction of suffix arrays and trees with many applications in string processing, computational biology and compression. A bottleneck in these applications is the amount of workspace needed to perform suffix sorting beyond the space needed to store the input as well as the output. In particular, emphasis is even on the constant c in the O(n) = cn space algorithms known for this problem. Currently the best previous result [5] takes O(nv + n log n) time and O(n/√v) extra space, for any v ∈ [1,√n] for strings from a general alphabet. We improve this and present the first known in-place suffix sorting algorithm. Our algorithm takes O(n log n) time using O(1) workspace and is optimal in the worst case for the general alphabet.

Implicit B-trees: New results for the dictionary problem

We reopen the issue of finding an implicit data structure for the dictionary problem. In particular, we examine the problem of maintaining n data values in the first n locations of an array in such a way that we can efficiently perform the operations insert, delete and search. No information other than n and the data is to be retained; and the only operations which we may perform on the data values (other than reads and writes) are comparisons. Our structure supports these operations in O(log/sup 2/ n/log log n) time, marking the first improvement on the problem since the mid 1980s. En route we develop a number of space efficient techniques for handling segments of a large array in a memory hierarchy. We achieve a cost of O(log/sub B/ n) block transfers like in regular B-trees, under the realistic assumption that a block stores B = /spl Omega/(log n) keys, so that reporting r consecutive keys in sorted order has a cost of O(log/sub B/n+r/B) block transfers. Being implicit, our B-tree occupies exactly [n/B] blocks after each update.

An in-place sorting with O(n log n) comparisons and O(n) moves

We present the first in-place algorithm for sorting an array of size n that performs, in the worst case, at most O(n log n) element comparisons and O(n) element transports. This solves a long-standing open problem, stated explicitly, e.g., in J.I. Munro and V. Raman (1992), of whether there exists a sorting algorithm that matches the asymptotic lower bounds on all computational resources simultaneously.

Optimal cache-oblivious implicit dictionaries

We consider the issues of implicitness and cache-obliviousness in the classical dictionary problem for n distinct keys over an unbounded and ordered universe. One finding in this paper is that of closing the longstanding open problem about the existence of an optimal implicit dictionary over an unbounded universe. Another finding is motivated by the antithetic features of implicit and cache-oblivious models in data structures. We show how to blend their best qualities achieving O(log n) time and O(logB n) block transfers for searching and for amortized updating, while using just n memory cells like sorted arrays and heaps. As a result, we avoid space wasting and provide fast data access at any level of the memory hierarchy.

Sorting Stably, in Place, with O(n log n) Comparisons and O(n) Moves

We settle a long-standing open question, namely whether it is possible to sort a sequence of n elements stably (i.e., preserving the original relative order of the equal elements), using O(1) auxiliary space and performing O(n log n) comparisons and O(n) data moves. Munro and Raman stated this problem in J. Algorithms (13, 1992) and gave an in-place but unstable sorting algorithm that performs O(n) data moves and O(n1+ε) comparisons. Subsequently (Algorithmica, 16, 1996) they presented a stable algorithm with these same bounds. Recently, Franceschini and Geffert (FOCS 2003) presented an unstable sorting algorithm that matches the asymptotic lower bounds on all computational resources.

Implicit B-trees: a new data structure for the dictionary problem

An implicit data structure for the dictionary problem maintains n delta values in the first n locations of an array in such a way that it efficiently supports the operations insert, delete and search. No information other than that in O(1) memory cells and in the input data is to be retained; and the only operations performed on the data values (other than reads and writes) are comparisons. This paper describes the implicit B-tree, a new data structure supporting these operations in O(logBn) block transfers like in regular B-trees, under the realistic assumption that a block stores B = Ω(log n) keys, so that reporting r consecutive keys in sorted order has a cost of O(logB n + r/B) block transfers. En route a number of space efficient techniques for handling segments of a large array in a memory hierarchy are developed. Being implicit, the proposed data structure occupies exactly |n/B| blocks of memory after each update, where n is the number of keys after each update and B is the number of keys contained in a memory block. In main memory, the time complexity of the operations is O(log2 n/log log n), disproving a conjecture of the mid 1980s.

Optimal suffix selection

Given a string S[1·s n], the suffix selection problemis to find the kth lexicographically smallest amongst the n suffixes S[i·s n], for i=1,...,n. In particular, the fundamental question is if selection can be performed more efficiently than sorting all the suffixes. If one considered n numbers, they can be sorted using Θ(n log n) comparisonsand the classical result from 70s is that selection can be done using O(n) comparisons. Thus selection is provably more efficient than sorting, for n numbers. Suffix sorting can be done using Θ(n log n) comparisons, but does suffix selection need suffix sorting? We settle this fundamental problem by presenting an optimal, deterministic algorithm for suffix selection using O(n) comparisons.