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On a Simple DepthFirst Search Strategy for Exploring Unknown Graphs
 In Proceedings of the Workshop on Algorithms and Data Structures
, 1997
"... . We present a simple depthfirst search strategy for exploring (constructing) an unknown strongly connected graph G with m edges and n vertices by traversing at most min(mn;dn 2 +m) edges. Here, d is the minimum number of edges needed to add to G to make it Eulerian. This parameter d is known as ..."
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Cited by 9 (0 self)
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. We present a simple depthfirst search strategy for exploring (constructing) an unknown strongly connected graph G with m edges and n vertices by traversing at most min(mn;dn 2 +m) edges. Here, d is the minimum number of edges needed to add to G to make it Eulerian. This parameter d is known
A framebased ratedistortion optimal coding system using a lower bound depthfirstsearch strategy
 in Proc. of Nordic Signal Processing Symposium
, 2002
"... The problem of finding the optimal set of quantized coefficients for a framebased encoded signal is known to be of very high complexity. This paper presents an efficient method of finding the operational RateDistortion (RD) optimal set of coefficients. The major complexity reduction lies in the r ..."
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Cited by 1 (1 self)
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The problem of finding the optimal set of quantized coefficients for a framebased encoded signal is known to be of very high complexity. This paper presents an efficient method of finding the operational RateDistortion (RD) optimal set of coefficients. The major complexity reduction lies in the reformulation of the original RDtradeoff problem, where a new set of coefficients is used as decision variables. These coefficients are connected to the orthogonalization of the set of selected frame vectors and not to the frame vectors themselves. Contrary to the original problem, the new problem is practicable to solve optimal in a reasonable amount of time. By organizing all possible solutions as nodes in a solution tree, we use complexity saving techniques to find the optimal solution in an even more efficient way. 1.
gSpan: GraphBased Substructure Pattern Mining
, 2002
"... We investigate new approaches for frequent graphbased pattern mining in graph datasets and propose a novel algorithm called gSpan (graphbased Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and ..."
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Cited by 650 (34 self)
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, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order, gSpan adopts the depthfirst search strategy to mine frequent connected subgraphs efficiently. Our performance study shows that gSpan substantially outperforms previous algorithms, sometimes
Sequential PAttern Mining using A Bitmap Representation
, 2002
"... We introduce a new algorithm for mining sequential patterns. Our algorithm is especially efficient when the sequential patterns in the database are very long. We introduce a novel depthfirst search strategy that integrates a depthfirst traversal of the search space with effective pruning mechanism ..."
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Cited by 207 (0 self)
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We introduce a new algorithm for mining sequential patterns. Our algorithm is especially efficient when the sequential patterns in the database are very long. We introduce a novel depthfirst search strategy that integrates a depthfirst traversal of the search space with effective pruning
Depthfirst IterativeDeepening: An Optimal Admissible Tree Search
 Artificial Intelligence
, 1985
"... The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadthfirst search requires too much space and depthfirst search can use too much time and doesn't always find a cheapest path. A depthfirst iteratiwdeepening a ..."
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Cited by 527 (24 self)
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The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadthfirst search requires too much space and depthfirst search can use too much time and doesn't always find a cheapest path. A depthfirst iteratiw
Depth first search and linear graph algorithms
 SIAM JOURNAL ON COMPUTING
, 1972
"... The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components of an undirect ..."
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Cited by 1406 (19 self)
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The value of depthfirst search or "backtracking" as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected components of a directed graph and ar algorithm for finding the biconnected components
TABU SEARCH
"... Tabu Search is a metaheuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. One of the main components of tabu search is its use of adaptive memory, which creates a more flexible search behavior. Memory based strategies are therefore the hallm ..."
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Cited by 822 (48 self)
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Tabu Search is a metaheuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. One of the main components of tabu search is its use of adaptive memory, which creates a more flexible search behavior. Memory based strategies are therefore
Tabu Search  Part I
, 1989
"... This paper presents the fundamental principles underlying tabu search as a strategy for combinatorial optimization problems. Tabu search has achieved impressive practical successes in applications ranging from scheduling and computer channel balancing to cluster analysis and space planning, and more ..."
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Cited by 680 (11 self)
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This paper presents the fundamental principles underlying tabu search as a strategy for combinatorial optimization problems. Tabu search has achieved impressive practical successes in applications ranging from scheduling and computer channel balancing to cluster analysis and space planning
On the Best Search Strategy in Parallel BranchandBound  BestFirstSearch vs. Lazy DepthFirstSearch.
 Annals of Operations Research
, 1996
"... or because pruning and evaluation tests are more effective in DFS due to the presence of better incumbents. 1 Introduction. One of the key issues of searchbased algorithms in general and B&Balgorithms in particular is the search strategy employed: In which order should the unexplored parts ..."
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Cited by 24 (4 self)
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Strategies in Parallel Branch and Bound 2 In parallel B&B one often regards the BestFirstSearch strategy (BeFS) and the DepthFirstSearch strategy (DFS) to be two of the prime candidates  BeFS due to expectations of efficiency and theoretical properties regarding anomalies, and DFS for reasons
Greedy Randomized Adaptive Search Procedures
, 2002
"... GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phas ..."
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Cited by 647 (82 self)
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phase. The best overall solution is kept as the result. In this chapter, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different applications. Enhanced or alternative
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