Results 1  10
of
20
Reach for A∗: Efficient pointtopoint shortest path algorithms
 IN WORKSHOP ON ALGORITHM ENGINEERING & EXPERIMENTS
, 2006
"... We study the pointtopoint shortest path problem in a setting where preprocessing is allowed. We improve the reachbased approach of Gutman [16] in several ways. In particular, we introduce a bidirectional version of the algorithm that uses implicit lower bounds and we add shortcut arcs which reduc ..."
Abstract

Cited by 62 (5 self)
 Add to MetaCart
We study the pointtopoint shortest path problem in a setting where preprocessing is allowed. We improve the reachbased approach of Gutman [16] in several ways. In particular, we introduce a bidirectional version of the algorithm that uses implicit lower bounds and we add shortcut arcs which reduce vertex reaches. Our modifications greatly reduce both preprocessing and query times. The resulting algorithm is as fast as the best previous method, due to Sanders and Schultes [27]. However, our algorithm is simpler and combines in a natural way with A∗ search, which yields significantly better query times.
Experimental analysis of dynamic all pairs shortest path algorithms
 In Proceedings of the fifteenth annual ACMSIAM symposium on Discrete algorithms
, 2004
"... We present the results of an extensive computational study on dynamic algorithms for all pairs shortest path problems. We describe our implementations of the recent dynamic algorithms of King and of Demetrescu and Italiano, and compare them to the dynamic algorithm of Ramalingam and Reps and to stat ..."
Abstract

Cited by 35 (4 self)
 Add to MetaCart
We present the results of an extensive computational study on dynamic algorithms for all pairs shortest path problems. We describe our implementations of the recent dynamic algorithms of King and of Demetrescu and Italiano, and compare them to the dynamic algorithm of Ramalingam and Reps and to static algorithms on random, realworld and hard instances. Our experimental data suggest that some of the dynamic algorithms and their algorithmic techniques can be really of practical value in many situations. 1
Fast and Flexible Difference Constraint Propagation for DPLL(T)
 IN PROC. SAT, VOLUME 4121 OF LNCS
, 2006
"... In the context of DPLL(T), theory propagation is the process of dynamically selecting consequences of a conjunction of constraints from a given set of candidate constraints. We present improvements to a fast theory propagation procedure for difference constraints of the form x − y ≤ c. These improve ..."
Abstract

Cited by 22 (1 self)
 Add to MetaCart
In the context of DPLL(T), theory propagation is the process of dynamically selecting consequences of a conjunction of constraints from a given set of candidate constraints. We present improvements to a fast theory propagation procedure for difference constraints of the form x − y ≤ c. These improvements are demonstrated experimentally.
Combining SpeedUp Techniques for ShortestPath Computations
 In Proc. 3rd Workshop on Experimental and Efficient Algorithms. LNCS
, 2004
"... Computing a shortest path from one node to another in a directed graph is a very common task in practice. This problem is classically solved by Dijkstra's algorithm. Many techniques are known to speed up this algorithm heuristically, while optimality of the solution can still be guaranteed. ..."
Abstract

Cited by 22 (6 self)
 Add to MetaCart
Computing a shortest path from one node to another in a directed graph is a very common task in practice. This problem is classically solved by Dijkstra's algorithm. Many techniques are known to speed up this algorithm heuristically, while optimality of the solution can still be guaranteed. In most studies, such techniques are considered individually.
An experimental study of a parallel shortest path algorithm for solving largescale graph instances
 Ninth Workshop on Algorithm Engineering and Experiments (ALENEX 2007)
, 2007
"... We present an experimental study of the single source shortest path problem with nonnegative edge weights (NSSP) on largescale graphs using the $\Delta$stepping parallel algorithm. We report performance results on the Cray MTA2, a multithreaded parallel computer. The MTA2 is a highend shared m ..."
Abstract

Cited by 12 (3 self)
 Add to MetaCart
We present an experimental study of the single source shortest path problem with nonnegative edge weights (NSSP) on largescale graphs using the $\Delta$stepping parallel algorithm. We report performance results on the Cray MTA2, a multithreaded parallel computer. The MTA2 is a highend shared memory system offering two unique features that aid the efficient parallel implementation of irregular algorithms: the ability to exploit finegrained parallelism, and lowoverhead synchronization primitives. Our implementation exhibits remarkable parallel speedup when compared with competitive sequential algorithms, for lowdiameter sparse graphs. For instance, $\Delta$stepping on a directed scalefree graph of 100 million vertices and 1 billion edges takes less than ten seconds on 40 processors of the MTA2, with a relative speedup of close to 30. To our knowledge, these are the first performance results of a shortest path problem on realistic graph instances in the order of billions of vertices and edges.
SpeedUp Techniques for ShortestPath Computations
 IN PROCEEDINGS OF THE 24TH INTERNATIONAL SYMPOSIUM ON THEORETICAL ASPECTS OF COMPUTER SCIENCE (STACS’07
, 2007
"... During the last years, several speedup techniques for Dijkstra’s algorithm have been published that maintain the correctness of the algorithm but reduce its running time for typical instances. They are usually based on a preprocessing that annotates the graph with additional information which can ..."
Abstract

Cited by 12 (6 self)
 Add to MetaCart
During the last years, several speedup techniques for Dijkstra’s algorithm have been published that maintain the correctness of the algorithm but reduce its running time for typical instances. They are usually based on a preprocessing that annotates the graph with additional information which can be used to prune or guide the search. Timetable information in public transport is a traditional application domain for such techniques. In this paper, we provide a condensed overview of new developments and extensions of classic results. Furthermore, we discuss how combinations of speedup techniques can be realized to take advantage from different strategies.
Experimental Evaluation of a New Shortest Path Algorithm (Extended Abstract)
, 2002
"... We evaluate the practical eciency of a new shortest path algorithm for undirected graphs which was developed by the rst two authors. This algorithm works on the fundamental comparisonaddition model. Theoretically, this new algorithm outperforms Dijkstra's algorithm on sparse graphs for t ..."
Abstract

Cited by 12 (4 self)
 Add to MetaCart
We evaluate the practical eciency of a new shortest path algorithm for undirected graphs which was developed by the rst two authors. This algorithm works on the fundamental comparisonaddition model. Theoretically, this new algorithm outperforms Dijkstra's algorithm on sparse graphs for the allpairs shortest path problem, and more generally, for the problem of computing singlesource shortest paths from !(1) different sources. Our extensive experimental analysis demonstrates that this is also the case in practice. We present results which show the new algorithm to run faster than Dijkstra's on a variety of sparse graphs when the number of vertices ranges from a few thousand to a few million, and when computing singlesource shortest paths from as few as three different sources.
Parallel Shortest Path Algorithms for Solving . . .
, 2006
"... We present an experimental study of the single source shortest path problem with nonnegative edge weights (NSSP) on largescale graphs using the ∆stepping parallel algorithm. We report performance results on the Cray MTA2, a multithreaded parallel computer. The MTA2 is a highend shared memory s ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
We present an experimental study of the single source shortest path problem with nonnegative edge weights (NSSP) on largescale graphs using the ∆stepping parallel algorithm. We report performance results on the Cray MTA2, a multithreaded parallel computer. The MTA2 is a highend shared memory system offering two unique features that aid the efficient parallel implementation of irregular algorithms: the ability to exploit finegrained parallelism, and lowoverhead synchronization primitives. Our implementation exhibits remarkable parallel speedup when compared with competitive sequential algorithms, for lowdiameter sparse graphs. For instance, ∆stepping on a directed scalefree graph of 100 million vertices and 1 billion edges takes less than ten seconds on 40 processors of the MTA2, with a relative speedup of close to 30. To our knowledge, these are the first performance results of a shortest path problem on realistic graph instances in the order of billions of vertices and edges.
Finding dominators in practice
 In Proceedings of the 12th Annual European Symposium on Algorithms, volume 3221 of Lecture Notes in Computer Science
, 2004
"... Abstract. The computation of dominators in a flowgraph has applications in program optimization, circuit testing, and other areas. Lengauer and Tarjan [17] proposed two versions of a fast algorithm for finding dominators and compared them experimentally with an iterative bit vector algorithm. They c ..."
Abstract

Cited by 7 (2 self)
 Add to MetaCart
Abstract. The computation of dominators in a flowgraph has applications in program optimization, circuit testing, and other areas. Lengauer and Tarjan [17] proposed two versions of a fast algorithm for finding dominators and compared them experimentally with an iterative bit vector algorithm. They concluded that both versions of their algorithm were much faster than the bitvector algorithm even on graphs of moderate size. Recently Cooper et al. [9] have proposed a new, simple, treebased iterative algorithm. Their experiments suggested that it was faster than the simple version of the LengauerTarjan algorithm on graphs representing computer program control flow. Motivated by the work of Cooper et al., we present an experimental study comparing their algorithm (and some variants) with careful implementations of both versions of the LengauerTarjan algorithm and with a new hybrid algorithm. Our results suggest that, although the performance of all the algorithms is similar, the most consistently fast are the simple LengauerTarjan algorithm and the hybrid algorithm, and their advantage increases as the graph gets bigger or more complicated. 1
PointtoPoint Shortest Path Algorithms with Preprocessing
"... Abstract. This is a survey of some recent results on pointtopoint shortest path algorithms. This classical optimization problem received a lot of attention lately and significant progress has been made. After an overview of classical results, we study recent heuristics that solve the problem while ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
Abstract. This is a survey of some recent results on pointtopoint shortest path algorithms. This classical optimization problem received a lot of attention lately and significant progress has been made. After an overview of classical results, we study recent heuristics that solve the problem while examining only a small portion of the input graph; the graph can be very big. Note that the algorithms we discuss find exact shortest paths. These algorithms are heuristic because they perform well only on some graph classes. While their performance has been good in experimental studies, no theoretical bounds are known to support the experimental observations. Most of these algorithms have been motivated by finding paths in large road networks. We start by reviewing the classical Dijkstra’s algorithm and its bidirectional variant, developed in 1950’s and 1960’s. Then we review A* search, an AI technique developed in 1970’s. Next we turn our attention to modern results which are based on preprocessing the graph. To be practical, preprocessing needs to be reasonably fast and not use too much space. We discuss landmark and reachbased algorithms as well as their combination. 1