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Deterministic Dictionaries
, 2001
"... It is shown that a static dictionary that offers constanttime access to n elements with wbit keys and occupies O(n) words of memory can be constructed deterministically in O(n log n) time on a unitcost RAM with word length w and a standard instruction set including multiplication. Whereas a rando ..."
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Cited by 34 (4 self)
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It is shown that a static dictionary that offers constanttime access to n elements with wbit keys and occupies O(n) words of memory can be constructed deterministically in O(n log n) time on a unitcost RAM with word length w and a standard instruction set including multiplication. Whereas a randomized construction working in linear expected time was known, the running time of the best previous deterministic algorithm was Ω(n²). Using a standard dynamization technique, the first deterministic dynamic dictionary with constant lookup time and sublinear update time is derived. The new algorithms are weakly nonuniform; i.e., they require access to a fixed number of precomputed constants dependent on w. The main technical tools employed are unitcost errorcorrecting codes, word parallelism, and derandomization using conditional expectations.
Dynamic Ordered Sets with Exponential Search Trees
 Combination of results presented in FOCS 1996, STOC 2000 and SODA
, 2001
"... We introduce exponential search trees as a novel technique for converting static polynomial space search structures for ordered sets into fullydynamic linear space data structures. This leads to an optimal bound of O ( √ log n/log log n) for searching and updating a dynamic set of n integer keys i ..."
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Cited by 26 (1 self)
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We introduce exponential search trees as a novel technique for converting static polynomial space search structures for ordered sets into fullydynamic linear space data structures. This leads to an optimal bound of O ( √ log n/log log n) for searching and updating a dynamic set of n integer keys in linear space. Here searching an integer y means finding the maximum key in the set which is smaller than or equal to y. This problem is equivalent to the standard text book problem of maintaining an ordered set (see, e.g., Cormen, Leiserson, Rivest, and Stein: Introduction to Algorithms, 2nd ed., MIT Press, 2001). The best previous deterministic linear space bound was O(log n/log log n) due Fredman and Willard from STOC 1990. No better deterministic search bound was known using polynomial space.
Subquadratic algorithms for 3SUM
 In Proc. 9th Worksh. Algorithms & Data Structures, LNCS 3608
, 2005
"... We obtain subquadratic algorithms for 3SUM on integers and rationals in several models. On a standard word RAM with wbit words, we obtain a running time of O(n 2 / max { w lg 2 w, lg 2 n (lg lg n) 2}). In the circuit RAM with one nonstandard AC0 operation, we obtain O(n2 / w2 lg2). In external w me ..."
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Cited by 13 (2 self)
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We obtain subquadratic algorithms for 3SUM on integers and rationals in several models. On a standard word RAM with wbit words, we obtain a running time of O(n 2 / max { w lg 2 w, lg 2 n (lg lg n) 2}). In the circuit RAM with one nonstandard AC0 operation, we obtain O(n2 / w2 lg2). In external w memory, we achieve O(n2 /(MB)), even under the standard assumption of data indivisibility. Cacheobliviously, we obtain a running time of O(n2 / MB lg2). In all cases, our speedup is almost M quadratic in the parallelism the model can afford, which may be the best possible. Our algorithms are Las Vegas randomized; time bounds hold in expectation, and in most cases, with high probability. 1
Generic Discrimination  Sorting and Partitioning Unshared Data in Linear Time
, 2008
"... We introduce the notion of discrimination as a generalization of both sorting and partitioning and show that worstcase lineartime discrimination functions (discriminators) can be defined generically, by (co)induction on an expressive language of order denotations. The generic definition yields di ..."
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Cited by 4 (3 self)
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We introduce the notion of discrimination as a generalization of both sorting and partitioning and show that worstcase lineartime discrimination functions (discriminators) can be defined generically, by (co)induction on an expressive language of order denotations. The generic definition yields discriminators that generalize both distributive sorting and multiset discrimination. The generic discriminator can be coded compactly using list comprehensions, with order denotations specified using Generalized Algebraic Data Types (GADTs). A GADTfree combinator formulation of discriminators is also given. We give some examples of the uses of discriminators, including a new mostsignificantdigit lexicographic sorting algorithm. Discriminators generalize binary comparison functions: They operate on n arguments at a time, but do not expose more information than the underlying equivalence, respectively ordering relation on the arguments. We argue that primitive types with equality (such as references in ML) and ordered types (such as the machine integer type), should expose their equality, respectively standard ordering relation, as discriminators: Having only a binary equality test on a type requires Θ(n 2) time to find all the occurrences of an element in a list of length n, for each element in the list, even if the equality test takes only constant time. A discriminator accomplishes this in linear time. Likewise, having only a (constanttime) comparison function requires Θ(n log n) time to sort a list of n elements. A discriminator can do this in linear time.
Lower Bound Techniques for Data Structures
, 2008
"... We describe new techniques for proving lower bounds on datastructure problems, with the following broad consequences:
â¢ the first Î©(lgn) lower bound for any dynamic problem, improving on a bound that had been standing since 1989;
â¢ for static data structures, the first separation between linea ..."
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Cited by 1 (0 self)
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We describe new techniques for proving lower bounds on datastructure problems, with the following broad consequences:
â¢ the first Î©(lgn) lower bound for any dynamic problem, improving on a bound that had been standing since 1989;
â¢ for static data structures, the first separation between linear and polynomial space. Specifically, for some problems that have constant query time when polynomial space is allowed, we can show Î©(lg n/ lg lg n) bounds when the space is O(n Â· polylog n).
Using these techniques, we analyze a variety of central datastructure problems, and obtain improved lower bounds for the following:
â¢ the partialsums problem (a fundamental application of augmented binary search trees);
â¢ the predecessor problem (which is equivalent to IP lookup in Internet routers);
â¢ dynamic trees and dynamic connectivity;
â¢ orthogonal range stabbing;
â¢ orthogonal range counting, and orthogonal range reporting;
â¢ the partial match problem (searching with wildcards);
â¢ (1 + Îµ)approximate near neighbor on the hypercube;
â¢ approximate nearest neighbor in the lâ metric.
Our new techniques lead to surprisingly nontechnical proofs. For several problems, we obtain simpler proofs for bounds that were already known.
Randomized Algorithms for Geometric Optimization Problems
, 2000
"... This chapter reviews randomization algorithms developed in the last few years to solve a wide range of geometric optimization problems. We review a number of general techniques, including randomized binary search, randomized linearprogramming algorithms, and random sampling. Next, we describe sever ..."
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This chapter reviews randomization algorithms developed in the last few years to solve a wide range of geometric optimization problems. We review a number of general techniques, including randomized binary search, randomized linearprogramming algorithms, and random sampling. Next, we describe several applications of these techniques, including facility location, proximity problems, nearest neighbor searching, statistical estimators, and Euclidean TSP.
Making Deterministic Signatures Quickly
"... We present a new technique of universe reduction. Primary applications are the dictionary problem and the predecessor problem. We give several new results on static dictionaries in different computational models: the Word RAM, the Practical RAM, and the cacheoblivious model. All algorithms and data ..."
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We present a new technique of universe reduction. Primary applications are the dictionary problem and the predecessor problem. We give several new results on static dictionaries in different computational models: the Word RAM, the Practical RAM, and the cacheoblivious model. All algorithms and data structures are deterministic and use linear space. Representative results are: a dictionary with a lookup time of O(log log n) and construction time of O(n) on sorted input on a Word RAM, and a static predecessor structure for variable and unbounded length binary strings that in the cacheoblivious model has a query performance of O ( s + log s) I/Os, for B query argument s.