- DFS first traverses nodes going through one adjacent of root, then next adjacent. The problem with this approach is, if there is a node close to root, but not in first few subtrees explored by DFS, then DFS reaches that node very late. Also, DFS may not find shortest path to a node (in terms of number of edges).
- BFS goes level by level, but requires more space. The space required by DFS is O(d) where d is depth of tree, but space required by BFS is O(n) where n is number of nodes in tree (Why? Note that the last level of tree can have around n/2 nodes and second last level n/4 nodes and in BFS we need to have every level one by one in queue).
IDDFS combines depth-first search’s space-efficiency and breadth-first search’s fast search (for nodes closer to root).
How does IDDFS work?
IDDFS calls DFS for different depths starting from an initial value. In every call, DFS is restricted from going beyond given depth. So basically we do DFS in a BFS fashion.
// Returns true if target is reachable from // src within max_depth bool IDDFS(src, target, max_depth) for limit from 0 to max_depth if DLS(src, target, limit) == true return true return false bool DLS(src, target, limit) if (src == target) return true; // If reached the maximum depth, // stop recursing. if (limit <= 0) return false; foreach adjacent i of src if DLS(i, target, limit?1) return true return false
An important thing to note is, we visit top level nodes multiple times. The last (or max depth) level is visited once, second last level is visited twice, and so on. It may seem expensive, but it turns out to be not so costly, since in a tree most of the nodes are in the bottom level. So it does not matter much if the upper levels are visited multiple times.
Below is implementation of above algorithm
Target is reachable from source within max depth
There can be two cases-
a) When the graph has no cycle: This case is simple. We can DFS multiple times with different height limits.
Time Complexity: Suppose we have a tree having branching factor ‘b’ (number of children of each node), and its depth ‘d’, i.e., there are bd nodes.
In an iterative deepening search, the nodes on the bottom level are expanded once, those on the next to bottom level are expanded twice, and so on, up to the root of the search tree, which is expanded d+1 times. So the total number of expansions in an iterative deepening search is-
(d)b + (d-1)b2 + .... + 3bd-2 + 2bd-1 + bd That is, Summation[(d + 1 - i) bi], from i = 0 to i = d Which is same as O(bd)
After evaluating the above expression, we find that asymptotically IDDFS takes the same time as that of DFS and BFS, but it is indeed slower than both of them as it has a higher constant factor in its time complexity expression.
IDDFS is best suited for a complete infinite tree
This article is contributed by Rachit Belwariar. If you like GeeksforGeeks and would like to contribute, you can also write an article and mail your article to firstname.lastname@example.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above
- Iterative Depth First Traversal of Graph
- Iterative approach to check for children sum property in a Binary Tree
- Applications of Depth First Search
- Depth First Search or DFS for a Graph
- Top 10 Interview Questions on Depth First Search (DFS)
- Flatten a multi-level linked list | Set 2 (Depth wise)
- Print nodes having maximum and minimum degrees
- Widest Path Problem | Practical application of Dijkstra's Algorithm
- Convert Directed Graph into a Tree
- Implementation of BFS using adjacency matrix
- Implementation of DFS using adjacency matrix
- Find the minimum number of moves to reach end of the array
- Count of numbers in the range [L, R] which satisfy the given conditions
- Find if a degree sequence can form a simple graph | Havel-Hakimi Algorithm