This paper is a study of persistence in data structures. Ordinary data structures are ephemeral in the sense that a change to the structure destroys the old version, leaving only the new version available for use. In contrast, a persistent structure allows access to any version, old or new, at any time. We develop simple, systematic, and effiient techniques for making linked data structures persistent. We use our techniques to devise persistent forms of binary search trees with logarithmic access, insertion, and deletion times and O(1) space bounds for insertion and deletion.

1 Introduction: the role of the user in heuristics Most state-of-the-art heuristics are characterized by a certain number of choices and free parameters, whose appropriate setting is a subject that raises issues of research methodology [5, 41, 51]. In some cases, these parameters are tuned through a feedback loop that includes the user as a crucial learning component: depending on preliminary algorithm tests some parameter values are changed by the

First we present a generalization of symmetric binary B-trees, SBB(k)- trees. The obtained structure has a height of only \Sigma (1 + 1k) log(n + 1)\Upsilon, where k may be chosen to be any positive integer. The maintenance algorithms require only a constant number of rotations per updating operation in the worst case. These properties together with the fact that the structure is relatively simple to implement makes it a useful alternative to other search trees in practical applications. Then, by using an SBB(k)-tree with a varying k we achieve a structure with a logarithmic amortized cost per update and a height of log n + o(log n). This result is an improvement of the upper bound on the height of a dynamic binary search tree. By maintaining two trees simultaneously the amortized cost is transformed into a worstcase cost. Thus, we have improved the worst-case complexity of the dictionary problem.

We develop a new finger search tree with worst case constant update time in the Pointer Machine (PM) model of computation. This was a major problem in the field of Data Structures and was tantalizingly open for over twenty years, while many attempts by researchers were made to solve it. The result comes as a consequence of the innovative mechanism that guides the rebalancing operations, combined with incremental multiple splitting and fusion techniques over nodes.

Effective heuristic algorithmsfor combinatorial problems are based on integrating local neighborhood search with history-sensitive schemes, where the information collected during the previous search phase is used to direct the future effort. In particular, some algorithms (like Strict Tabu Search and Reactive Tabu Search) need to detect whether a configuration has already been encountered during the previous phase of the search, either to prohibit repetitions or to determine a prohibition parameter in an adaptive way. This paper analyzes the use of persistent dynamic sets for storing and retrieving states and discusses the advantages of this option with respect to popular but less efficient realizations. If the search space is given by L-bit binary strings, the method complexity is O(L) average-case time per search iteration when hashing is used, while the total space for a sequence of t iterations is O(t).

We develop a new class of weight balanced binary search trees called/?-balanced binary search trees (/?-BBSTs). /?-BBSTs are designed to have reduced internal path length. As a result, they are expected to exhibit good search time characteristics.

These proceedings contain the contributions presented at the 3rd DIKU-IST Joint Workshop on Foundations of Software held at Roskilde, Denmark, October 5-6, 2007. The workshop featured talks and discussions on domain-specific languages, logic and model checking, program complexity and optimization, reversible and bidirectional computing, and demonstrations of software prototypes.

We develop a new finger search tree with worst-case constant update time in the Pointer Machine (PM) model of computation. This was a major problem in the field of Data Structures and was tantalizingly open for over twenty years while many attempts by researchers were made to solve it. The result comes as a consequence of the innovative mechanism that guides the rebalancing operations combined with incremental multiple splitting and fusion techniques over nodes.