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Optimal Purely Functional Priority Queues
 JOURNAL OF FUNCTIONAL PROGRAMMING
, 1996
"... Brodal recently introduced the first implementation of imperative priority queues to support findMin, insert, and meld in O(1) worstcase time, and deleteMin in O(log n) worstcase time. These bounds are asymptotically optimal among all comparisonbased priority queues. In this paper, we adapt B ..."
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Cited by 18 (1 self)
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Brodal recently introduced the first implementation of imperative priority queues to support findMin, insert, and meld in O(1) worstcase time, and deleteMin in O(log n) worstcase time. These bounds are asymptotically optimal among all comparisonbased priority queues. In this paper, we adapt Brodal's data structure to a purely functional setting. In doing so, we both simplify the data structure and clarify its relationship to the binomial queues of Vuillemin, which support all four operations in O(log n) time. Specifically, we derive our implementation from binomial queues in three steps: first, we reduce the running time of insert to O(1) by eliminating the possibility of cascading links; second, we reduce the running time of findMin to O(1) by adding a global root to hold the minimum element; and finally, we reduce the running time of meld to O(1) by allowing priority queues to contain other priority queues. Each of these steps is expressed using MLstyle functors. The last transformation, known as datastructural bootstrapping, is an interesting application of higherorder functors and recursive structures.
Numerical Representations as HigherOrder Nested Datatypes
, 1998
"... Number systems serve admirably as templates for container types: a container object of size n is modelled after the representation of the number n and operations on container objects are modelled after their numbertheoretic counterparts. Binomial queues are probably the first data structure that wa ..."
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Cited by 5 (2 self)
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Number systems serve admirably as templates for container types: a container object of size n is modelled after the representation of the number n and operations on container objects are modelled after their numbertheoretic counterparts. Binomial queues are probably the first data structure that was designed with this analogy in mind. In this paper we show how to express these socalled numerical representations as higherorder nested datatypes. A nested datatype allows to capture the structural invariants of a numerical representation, so that the violation of an invariant can be detected at compiletime. We develop a programming method which allows to adapt algorithms to the new representation in a mostly straightforward manner. The framework is employed to implement three different container types: binary randomaccess lists, binomial queues, and 23 finger search trees. The latter data structure, which is treated in some depth, can be seen as the main innovation from a datastruct...
Redundant Call Elimination via Tupling
 FUNDAMENTA INFORMATICAE
, 2005
"... Redundant call elimination has been an important program optimisation process as it can produce superlinear speedup in optimised programs. In this paper, we investigate use of the tupling transformation in achieving this optimisation over a firstorder functional language. Standard tupling techniqu ..."
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Cited by 2 (0 self)
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Redundant call elimination has been an important program optimisation process as it can produce superlinear speedup in optimised programs. In this paper, we investigate use of the tupling transformation in achieving this optimisation over a firstorder functional language. Standard tupling technique, as described in [6], works excellently in a restricted variant of the language; namely, functions with single recursion argument. We provide a semantic understanding of call redundancy, upon which we construct an analysis for handling the tupling of functions with multiple recursion arguments. The analysis provides a means to ensure termination of the tupling transformation. As the analysis is of polynomial complexity, it makes the tupling suitable as a step in compiler optimisation.
Benchmarking Purely Functional Data Structures
 Journal of Functional Programming
, 1999
"... When someone designs a new data structure, they want to know how well it performs. Previously, the only way to do this involves finding, coding and testing some applications to act as benchmarks. This can be tedious and timeconsuming. Worse, how a benchmark uses a data structure may considerably af ..."
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Cited by 2 (0 self)
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When someone designs a new data structure, they want to know how well it performs. Previously, the only way to do this involves finding, coding and testing some applications to act as benchmarks. This can be tedious and timeconsuming. Worse, how a benchmark uses a data structure may considerably affect the efficiency of the data structure. Thus, the choice of benchmarks may bias the results. For these reasons, new data structures developed for functional languages often pay little attention to empirical performance. We solve these problems by developing a benchmarking tool, Auburn, that can generate benchmarks across a fair distribution of uses. We precisely define "the use of a data structure", upon which we build the core algorithms of Auburn: how to generate a benchmark from a description of use, and how to extract a description of use from an application. We consider how best to use these algorithms to benchmark competing data structures. Finally, we test Auburn by benchmarking ...
Compact Representation for Answer Sets of nary Regular Queries
"... An nary query over trees takes an input tree t and returns a set of ntuples of the nodes of t. In this paper, a compact data structure is introduced for representing the answer sets of nary queries defined by tree automata. Despite that the number of the elements of the answer set can be as large ..."
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An nary query over trees takes an input tree t and returns a set of ntuples of the nodes of t. In this paper, a compact data structure is introduced for representing the answer sets of nary queries defined by tree automata. Despite that the number of the elements of the answer set can be as large as t  n, our representation allows storing the set using only O(t) space. Several basic operations on the sets are shown to be efficiently executable on the representation.