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Generating Bracelets in Constant Amortized Time

by Joe Sawada - SIAM JOURNAL ON COMPUTING , 2001
"... A bracelet is the lexicographically smallest element in an equivalence class of strings under string rotation and reversal. We present a fast, simple, recursive algorithm for generating (i.e., listing) k-ary bracelets. Using simple bounding techniques, we prove that the algorithm is optimal in the s ..."
Abstract - Cited by 10 (3 self) - Add to MetaCart
in the sense that the running time is proportional to the number of bracelets produced. This is an improvement by a factor of n (where n is the length of the bracelets being generated) over the fastest, previously known algorithm to generate bracelets.

Generating Bracelets in Constant Amortized Time

by unknown authors , 2001
"... Abstract A bracelet is the lexicographically smallest element in an equivalence class of strings under string rotation and reversal. We present a fast, simple, recursive algorithm for generating (ie., listing) k-ary bracelets. Using simple bounding techniques, we prove that the algorithm is optimal ..."
Abstract - Add to MetaCart
in the sense that the running time is proportional to the number of bracelets produced. This is an improvement by a factor of n (where n is the length of the bracelets being generated) over the fastest, previously known algorithm to generate bracelets.

Secure Two-Party Computation in Sublinear (Amortized) Time

by S. Dov Gordon, Jonathan Katz, Fernando Krell, Mariana Raykova, Tal Malkin, Yevgeniy Vahlis, Vladimir Kolesnikov
"... Traditional approaches to generic secure computation begin by representing the function f being computed as a circuit. If f depends on each of its input bits, this implies a protocol with complexity at least linear in the input size. In fact, linear running time is inherent for non-trivial functions ..."
Abstract - Cited by 18 (3 self) - Add to MetaCart
present an approach to secure two-party computation that yields protocols running in sublinear time, in an amortized sense, for functions that can be computed in sublinear time on a random-access machine (RAM). Moreover, each party is required to maintain state that is only (essentially) linear in its own

Relaxed Fibonacci heaps: An alternative . . . amortized time bounds

by Chandrasekhar Boyapati, C. Pandu Rangan , 1995
"... ..."
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Abstract not found

Amortized Efficiency of List Update and Paging Rules

by Daniel D. Sleator, Robert E. Tarjan , 1985
"... In this article we study the amortized efficiency of the “move-to-front” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes 0(i) time, we show that move-to-front is within a constant factor of optimum amo ..."
Abstract - Cited by 824 (8 self) - Add to MetaCart
In this article we study the amortized efficiency of the “move-to-front” and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes 0(i) time, we show that move-to-front is within a constant factor of optimum

Dynamic Planar Convex Hull Operations in Near-Logarithmic Amortized Time

by Timothy M. Chan - JOURNAL OF THE ACM , 1999
"... We give a data structure that allows arbitrary insertions and deletions on a planar point set P and supports basic queries on the convex hull of P , such as membership and tangent-finding. Updates take O(log 1+" n) amortized time and queries take O(log n) time each, where n is the maximum siz ..."
Abstract - Cited by 40 (6 self) - Add to MetaCart
We give a data structure that allows arbitrary insertions and deletions on a planar point set P and supports basic queries on the convex hull of P , such as membership and tangent-finding. Updates take O(log 1+" n) amortized time and queries take O(log n) time each, where n is the maximum

Solving online feasibility problem in constant amortized time per update

by Lilian Buzer , 2005
"... We present a deterministic algorithm for solving the two and three-dimensional online feasibility problem. Insertion of a new constraint is processed in constant amortized time. Our method is adapted from the offline linear deterministic Megiddo algorithm for linear programming. As in his prune an ..."
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We present a deterministic algorithm for solving the two and three-dimensional online feasibility problem. Insertion of a new constraint is processed in constant amortized time. Our method is adapted from the offline linear deterministic Megiddo algorithm for linear programming. As in his prune

Why some heaps support constant-amortized-time decrease-key operations, . . .

by John Iacono , 2013
"... ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Abstract not found

A Gray code for fixed-density necklaces and Lyndon words in constant amortized time

by J. Sawada, A. Williams - Theoretical Computer Science
"... This paper develops a constant amortized time algorithm to produce the cyclic cool-lex Gray code for fixed-density binary necklaces, Lyndon words, and pseudo-necklaces. It is the first Gray code for these objects that achieves this time bound. The algorithm is applied: (i) to develop a constant amor ..."
Abstract - Cited by 4 (3 self) - Add to MetaCart
This paper develops a constant amortized time algorithm to produce the cyclic cool-lex Gray code for fixed-density binary necklaces, Lyndon words, and pseudo-necklaces. It is the first Gray code for these objects that achieves this time bound. The algorithm is applied: (i) to develop a constant

Fibonacci Heaps and Their Uses in Improved Network optimization algorithms

by Michael L. Fredman, Robert Endre Tarjan , 1987
"... In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, Fibonacci heaps (abbreviated F-heaps), extends the binomial queues proposed by Vuillemin and studied further by Brown. F-heaps support arbitrary deletion from an n-item heap in qlogn) amortized tim ..."
Abstract - Cited by 739 (18 self) - Add to MetaCart
time and all other standard heap operations in o ( 1) amortized time. Using F-heaps we are able to obtain improved running times for several network optimization algorithms. In particular, we obtain the following worst-case bounds, where n is the number of vertices and m the number of edges
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