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17
Fibonacci Heaps and Their Uses in Improved Network optimization algorithms
, 1987
"... In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, Fibonacci heaps (abbreviated Fheaps), extends the binomial queues proposed by Vuillemin and studied further by Brown. Fheaps support arbitrary deletion from an nitem heap in qlogn) amortized tim ..."
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Cited by 739 (18 self)
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In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, Fibonacci heaps (abbreviated Fheaps), extends the binomial queues proposed by Vuillemin and studied further by Brown. Fheaps support arbitrary deletion from an nitem heap in qlogn) amortized
Hollow Heaps
"... We introduce the hollow heap, a very simple data structure with the same amortized efficiency as the classical Fibonacci heap. All heap operations except delete and deletemin take O(1) time, worst case as well as amortized; delete and deletemin take O(log n) amortized time. Hollow heaps are by far ..."
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We introduce the hollow heap, a very simple data structure with the same amortized efficiency as the classical Fibonacci heap. All heap operations except delete and deletemin take O(1) time, worst case as well as amortized; delete and deletemin take O(log n) amortized time. Hollow heaps
Available Stabilizing Heaps
, 2001
"... This paper describes a heap construction that supports insert and delete operations in arbitrary (possibly illegitimate) states. After any sequence of at most O(m) heap operations, the heap state is guaranteed to be legitimate, where m is the initial number of items in the heap. The response from ea ..."
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Cited by 4 (1 self)
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each operation is consistent with its effect on the data structure, even for illegitimate states. The time complexity of each operation is O(lgK)where K is the capacity of the data structure; when the heap's state is legitimate the time complexity is O(lg n) for n equal to the number items
Very Fast Optimal Parallel Algorithms for Heap Construction
, 1994
"... We give two algorithms for permuting n items in an array into heap order on a CRCW PRAM. The first is deterministic and runs in O(log log n) time and performs O(n) operations. This runtime is the best possible for any comparisonbased algorithm using n processors. The second is randomized and runs ..."
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Cited by 4 (0 self)
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We give two algorithms for permuting n items in an array into heap order on a CRCW PRAM. The first is deterministic and runs in O(log log n) time and performs O(n) operations. This runtime is the best possible for any comparisonbased algorithm using n processors. The second is randomized
Parallel Priority Queues
, 1991
"... This paper introduces the Parallel Priority Queue (PPQ) abstract data type. A PPQ stores a set of integervalued items and provides operations such as insertion of n new items or deletion of the n smallest ones. Algorithms for realizing PPQ operations on an nprocessor CREWPRAM are based on two new ..."
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Cited by 14 (1 self)
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new data structures, the nBandwidthHeap (nH) and the nBandwidth LeftistHeap (nL), that are obtained as extensions of the well known sequential binaryheap and leftistheap, respectively. Using these structures, it is shown that insertion of n new items in a PPQ of m elements can be performed
Parallel Algorithms for Priority Queue Operations
 In SWAT'92 LNCS 621
, 1992
"... This paper presents parallel algorithms for priority queue operations on a pprocessor EREWPRAM. The algorithms are based on a new data structure, the Minpath Heap (MH), which is obtained as an extension of the traditional binaryheap organization. Using an MH, it is shown that insertion of a new i ..."
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Cited by 7 (0 self)
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item or deletion of the smallest item from a priority queue of n elements can be performed in O( log n p + log log n) parallel time, while construction of an MH from a set of n items takes O( n p + log n) time. The given algorithms for insertion and deletion achieve the best possible running time
Fast and Scalable Parallel Algorithms for KnapsackLike Problems
 JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
, 1996
"... We present two new algorithms for searching in sorted X+Y +R+S, one based on heaps and the other on sampling. Each of the algorithms runs in time O(n 2 logn) (n being the size of the sorted arrays X, Y , R and S). Hence in each case, by constructing arrays of size n = O(2 s=4 ), we obtain a new ..."
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Cited by 3 (0 self)
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We present two new algorithms for searching in sorted X+Y +R+S, one based on heaps and the other on sampling. Each of the algorithms runs in time O(n 2 logn) (n being the size of the sorted arrays X, Y , R and S). Hence in each case, by constructing arrays of size n = O(2 s=4 ), we obtain a new
Binary Tournaments and Priority Queues: PRAM and BSP
, 1997
"... We use an old idea of tournament based complete binary tree (CBT) to implement parallel priority queues (PQs). We show that this data structure enables a more efficient implementation of the operations extractmin and insert in terms of communications and synchronizations among processors than simil ..."
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Cited by 3 (3 self)
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similar operations on the implicit heap. In most cases we only improve the asymptotic bounds on constant factors. However, some operations can be twice faster using simpler parallel algorithms upon the CBT. 1 Data structure and basic operations Every item stored in the PQ consists of a priority value
Queaps
, 2002
"... We present a new priority queue data structure, the queap, that executes insertion in O(1) amortized time and extractmin in O(log(k + 2)) amortized time if there are k items that have been in the heap longer than the item to be extracted. Thus if the operations on the queap are rstin rstout ..."
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Cited by 1 (0 self)
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We present a new priority queue data structure, the queap, that executes insertion in O(1) amortized time and extractmin in O(log(k + 2)) amortized time if there are k items that have been in the heap longer than the item to be extracted. Thus if the operations on the queap are rstin rst
Median Filtering is Equivalent to Sorting
"... Abstract. This work shows that the following problems are equivalent, both in theory and in practice: • median filtering: given an nelement vector, compute the sliding window median with window size k, • piecewise sorting: given an nelement vector, divide it in n/k blocks of length k and sort each ..."
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the performance of the new sortingbased algorithm is on a par with the fastest heapbased algorithms, and for benign data distributions it typically outperforms prior algorithms. The key technical idea is that we can represent the sliding window with a pair of sorted doublylinked lists: we delete items from one
Results 1  10
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