Stochastic Modeling of Branch-and-Bound Algorithms with Best-First Search
IEEE Transactions on Software Engineering - Special issue on COMPSAC 1982 and 1983
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
The average complexity of depth-first search with backtracking and cutoff
IBM Journal of Research and Development
Phase transitions in artificial intelligence systems
Artificial Intelligence
Artificial Intelligence
Random Trees and the Analysis of Branch and Bound Procedures
Journal of the ACM (JACM)
An expected-cost analysis of backtracking and non-backtracking algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Searching for an optimal path in a tree with random costs
Artificial Intelligence
Search rearrangement backtracking and polynomial average time
Artificial Intelligence
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Motivated by an anomaly in branch-and-bound (BnB) search, we analyze its average-case complexity. We first delineate exponential vs polynomial average-case complexities of BnB. When best-first BnB is of linear complexity, we show that depth-first BnB has polynomial complexity. For problems on which best-first BnB has exponential complexity, we obtain an expression for the heuristic branching factor. Next, we apply our analysis to explain an anomaly in lookahead search on sliding-tile puzzles, and to predict the existence of an average-case complexity transition of BnB on the Asymmetric Traveling Salesman Problem. Finally, by formulating IDA* as costbounded BnB, we show its average-case optimality, which also implies that RBFS is optimal on average.