Results 1  10
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16
Exact and Approximate Distances in Graphs  a survey
 In ESA
, 2001
"... We survey recent and not so recent results related to the computation of exact and approximate distances, and corresponding shortest, or almost shortest, paths in graphs. We consider many different settings and models and try to identify some remaining open problems. ..."
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Cited by 57 (0 self)
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We survey recent and not so recent results related to the computation of exact and approximate distances, and corresponding shortest, or almost shortest, paths in graphs. We consider many different settings and models and try to identify some remaining open problems.
A Simple Shortest Path Algorithm with Linear Average Time
"... We present a simple shortest path algorithm. If the input lengths are positive and uniformly distributed, the algorithm runs in linear time. The worstcase running time of the algorithm is O(m + n log C), where n and m are the number of vertices and arcs of the input graph, respectively, and C i ..."
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Cited by 34 (6 self)
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We present a simple shortest path algorithm. If the input lengths are positive and uniformly distributed, the algorithm runs in linear time. The worstcase running time of the algorithm is O(m + n log C), where n and m are the number of vertices and arcs of the input graph, respectively, and C is the ratio of the largest and the smallest nonzero arc length.
Integer Priority Queues with Decrease Key in . . .
 STOC'03
, 2003
"... We consider Fibonacci heap style integer priority queues supporting insert and decrease key operations in constant time. We present a deterministic linear space solution that with n integer keys support delete in O(log log n) time. If the integers are in the range [0,N), we can also support delete i ..."
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Cited by 27 (2 self)
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We consider Fibonacci heap style integer priority queues supporting insert and decrease key operations in constant time. We present a deterministic linear space solution that with n integer keys support delete in O(log log n) time. If the integers are in the range [0,N), we can also support delete in O(log log N) time. Even for the special case of monotone priority queues, where the minimum has to be nondecreasing, the best previous bounds on delete were O((log n) 1/(3−ε) ) and O((log N) 1/(4−ε)). These previous bounds used both randomization and amortization. Our new bounds a deterministic, worstcase, with no restriction to monotonicity, and exponentially faster. As a classical application, for a directed graph with n nodes and m edges with nonnegative integer weights, we get single source shortest paths in O(m + n log log n) time, or O(m + n log log C) ifC is the maximal edge weight. The later solves an open problem of Ahuja, Mehlhorn, Orlin, and
Shortest Path Algorithms: Engineering Aspects
 In Proc. ESAAC ’01, Lecture Notes in Computer Science
, 2001
"... We review shortest path algorithms based on the multilevel bucket data structure [6] and discuss the interplay between theory and engineering choices that leads to e#cient implementations. Our experimental results suggest that the caliber heuristic [17] and adaptive parameter selection give an ..."
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Cited by 20 (3 self)
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We review shortest path algorithms based on the multilevel bucket data structure [6] and discuss the interplay between theory and engineering choices that leads to e#cient implementations. Our experimental results suggest that the caliber heuristic [17] and adaptive parameter selection give an e#cient algorithm, both on typical and on hard inputs, for a wide range of arc lengths.
Smoothed Analysis of Three Combinatorial Problems
 Proc. of the 28th Int. Symp. on Mathematical Foundations of Computer Science (MFCS), volume 2747 of Lecture Notes in Computer Science
, 2003
"... Smoothed analysis combines elements over worstcase and average case analysis. For an instance x the smoothed complexity is the average complexity of an instance obtained from x by a perturbation. The smoothed complexity of a problem is the worst smoothed complexity of any instance. Spielman and ..."
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Cited by 20 (1 self)
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Smoothed analysis combines elements over worstcase and average case analysis. For an instance x the smoothed complexity is the average complexity of an instance obtained from x by a perturbation. The smoothed complexity of a problem is the worst smoothed complexity of any instance. Spielman and Teng introduced this notion for continuous problems. We apply the concept to combinatorial problems and study the smoothed complexity of three classical discrete problems: quicksort, lefttoright maxima counting, and shortest paths. This opens a vast eld of nice analyses (using for example generating functions in the discrete case) which should lead to a better understanding of complexity landscapes of algorithms.
Computing Shortest Paths with Comparisons and Additions
 SODA
, 2002
"... We present an undirected allpairs shortest paths (APSP) algorithm which runs on a pointer machine in time O(mnot(m, n)) while making O(ran log a(m, n)) comparisons and additions, where m and n are the number of edges and vertices, respectively, and a(ra, n) is Tarjan's inverseAckermann function. ..."
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Cited by 19 (7 self)
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We present an undirected allpairs shortest paths (APSP) algorithm which runs on a pointer machine in time O(mnot(m, n)) while making O(ran log a(m, n)) comparisons and additions, where m and n are the number of edges and vertices, respectively, and a(ra, n) is Tarjan's inverseAckermann function. This improves upon all previous comparison & additionbased APSP algorithms when the graph is sparse, i.e., when m = o(n log n). At the heart of our APSP algorithm is a new singlesource shortest paths algorithm which runs in time O(ma(m,n) + nloglogr) on a pointer machine, where r is the ratio of the maximumtominimum edge length. So long as r < 2 '~°(a) this algorithm is faster than any implementation of Dijkstra's classical algorithm in the comparisonaddition model. For directed graphs we give an O(ra + nlogr)time comparison & additionbased SSSP algorithm on a pointer machine. Similar algorithms assuming integer weights or the RAM model were given earlier.
A shortest path algorithm for realweighted undirected graphs
 in 13th ACMSIAM Symp. on Discrete Algs
, 1985
"... Abstract. We present a new scheme for computing shortest paths on realweighted undirected graphs in the fundamental comparisonaddition model. In an efficient preprocessing phase our algorithm creates a linearsize structure that facilitates singlesource shortest path computations in O(m log α) ti ..."
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Cited by 12 (3 self)
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Abstract. We present a new scheme for computing shortest paths on realweighted undirected graphs in the fundamental comparisonaddition model. In an efficient preprocessing phase our algorithm creates a linearsize structure that facilitates singlesource shortest path computations in O(m log α) time, where α = α(m, n) is the very slowly growing inverseAckermann function, m the number of edges, and n the number of vertices. As special cases our algorithm implies new bounds on both the allpairs and singlesource shortest paths problems. We solve the allpairs problem in O(mnlog α(m, n)) time and, if the ratio between the maximum and minimum edge lengths is bounded by n (log n)O(1) , we can solve the singlesource problem in O(m + nlog log n) time. Both these results are theoretical improvements over Dijkstra’s algorithm, which was the previous best for real weighted undirected graphs. Our algorithm takes the hierarchybased approach invented by Thorup. Key words. singlesource shortest paths, allpairs shortest paths, undirected graphs, Dijkstra’s
Faster Kinetic Heaps and Their Use in Broadcast Scheduling (Extended Abstract)
 PROC. 12TH ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS, 2001
, 2001
"... We describe several implementations of the kinetic heap, a heap (priority queue) in which the key of each item, instead of being xed, is a linear function of time. The kinetic heap is a simple example of a kinetic data structure of the kind considered by Basch, Guibas, and Hershberger. Kinetic hea ..."
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Cited by 7 (0 self)
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We describe several implementations of the kinetic heap, a heap (priority queue) in which the key of each item, instead of being xed, is a linear function of time. The kinetic heap is a simple example of a kinetic data structure of the kind considered by Basch, Guibas, and Hershberger. Kinetic heaps have many applications in computational geometry, and previous implementations were designed to address these applications. We describe an additional application, to broadcast scheduling. Each of our kinetic heap implementations improves on previous implementations by being simpler or asymptotically faster for some or all applications.
Buckets, Heaps, Lists, and Monotone Priority Queues
, 1997
"... We introduce the heapontop (hot) priority queue data structure that combines the multilevel bucket data structure of Denardo and Fox and a heap. We use the new data structure to obtain an O(m + n(log C) 1 3 +ffl ) expected time implementation of Dijkstra's shortest path algorithm, improving the ..."
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Cited by 4 (0 self)
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We introduce the heapontop (hot) priority queue data structure that combines the multilevel bucket data structure of Denardo and Fox and a heap. We use the new data structure to obtain an O(m + n(log C) 1 3 +ffl ) expected time implementation of Dijkstra's shortest path algorithm, improving the previous bounds. We can implement hot queues even more efficiently in practice by using sorted lists to represent small priority queues. Our experimental results in the context of Dijkstra's algorithm show that this implementation of hot queues performs very well and is more robust than implementations based only on heap or multilevel bucket data structures. 1 Introduction A priority queue is a data structure that maintains a set of elements and supports operations insert, decreasekey, and extractmin. Priority queues are fundamental data structures with many applications. Typical applications include graph algorithms (e.g. [14]) and event simulation (e.g. [5]). An important subclass of...
A Practical Shortest Path Algorithm with Linear Expected Time
 SUBMITTED TO SIAM J. ON COMPUTING
, 2001
"... We present an improvement of the multilevel bucket shortest path algorithm of Denardo and Fox [9] and justify this improvement, both theoretically and experimentally. We prove that if the input arc lengths come from a natural probability distribution, the new algorithm runs in linear average time ..."
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Cited by 3 (1 self)
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We present an improvement of the multilevel bucket shortest path algorithm of Denardo and Fox [9] and justify this improvement, both theoretically and experimentally. We prove that if the input arc lengths come from a natural probability distribution, the new algorithm runs in linear average time while the original algorithm does not. We also describe an implementation of the new algorithm. Our experimental data suggests that the new algorithm is preferable to the original one in practice. Furthermore, for integral arc lengths that fit into a word of today's computers, the performance is close to that of breadthfirst search, suggesting limitations on further practical improvements.