. We present a simple depth-first 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

The problem of finding the optimal set of quan-tized coefficients for a frame-based encoded signal is known to be of very high complexity. This paper presents an efficient method of finding the operational Rate-Distortion (RD) optimal set of coefficients. The major complexity reduction lies in the reformulation of the original RD-tradeoff problem, where a new set of coefficients is used as decision variables. These co-efficients are connected to the orthogonalization of the set of selected frame vectors and not to the frame vec-tors themselves. Contrary to the original problem, the new problem is practicable to solve optimal in a rea-sonable amount of time. By organizing all possible solutions as nodes in a solution tree, we use complex-ity saving techniques to find the optimal solution in an even more efficient way. 1.

, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order, gSpan adopts the depth-first search strategy to mine frequent connected subgraphs efficiently. Our performance study shows that gSpan substantially outperforms previous algorithms, sometimes

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 depth-first search strategy that integrates a depth-first traversal of the search space with effective pruning

The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadth-first search requires too much space and depth-first search can use too much time and doesn't always find a cheapest path. A depth-first iteratiw

The value of depth-first 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 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

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

Strategies in Parallel Branch and Bound 2 In parallel B&B one often regards the Best-First-Search strategy (BeFS) and the Depth-First-Search strategy (DFS) to be two of the prime candidates - BeFS due to expectations of efficiency and theoretical properties regarding anomalies, and DFS for reasons

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