Time complexity of iterative-deepening-A∗ Original Research Article

We analyze the time complexity of iterative-deepening-A∗ (IDA∗). We first show how to calculate the exact number of nodes at a given depth of a regular search tree, and the asymptotic brute-force branching factor. We then use this result to analyze IDA∗ with a consistent, admissible heuristic function. Previous analyses relied on an abstract analytic model, and characterized the heuristic function in terms of its accuracy, but do not apply to concrete problems. In contrast, our analysis allows us to accurately predict the performance of IDA∗ on actual problems such as the sliding-tile puzzles and Rubikʹs Cube. The heuristic function is characterized by the distribution of heuristic values over the problem space. Contrary to conventional wisdom, our analysis shows that the asymptotic heuristic branching factor is the same as the brute-force branching factor. Thus, the effect of a heuristic function is to reduce the effective depth of search by a constant, relative to a brute-force search, rather than reducing the effective branching factor.