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Iterative deepening A* (IDA*) is a graph traversal and path search algorithm that can find the shortest path between a designated start node and any member of a set of goal nodes in a weighted graph. It is a variant of iterative deepening depth-first search that borrows the idea to use a heuristic function to conservatively estimate the ...
Iterative deepening prevents this loop and will reach the following nodes on the following depths, assuming it proceeds left-to-right as above: 0: A; 1: A, B, C, E (Iterative deepening has now seen C, when a conventional depth-first search did not.) 2: A, B, D, F, C, G, E, F (It still sees C, but that it came later.
For general graphs, replacing the stack of the iterative depth-first search implementation with a queue would also produce a breadth-first search algorithm, although a somewhat nonstandard one. [7] Another possible implementation of iterative depth-first search uses a stack of iterators of the list of neighbors of a node, instead of a stack of ...
In object-oriented programming, the iterator pattern is a design pattern in which an iterator is used to traverse a container and access the container's elements. The iterator pattern decouples algorithms from containers; in some cases, algorithms are necessarily container-specific and thus cannot be decoupled.
For a grid map from a video game, using the Taxicab distance or the Chebyshev distance becomes better depending on the set of movements available (4-way or 8-way). If the heuristic h satisfies the additional condition h ( x ) ≤ d ( x , y ) + h ( y ) for every edge ( x , y ) of the graph (where d denotes the length of that edge), then h is ...
Beam search with width 3 (animation) In computer science, beam search is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set.
In computer science, tree traversal (also known as tree search and walking the tree) is a form of graph traversal and refers to the process of visiting (e.g. retrieving, updating, or deleting) each node in a tree data structure, exactly once.
Any user-defined class can support standard iteration (either implicit or explicit) by defining an __iter__() method that returns an iterator object. The iterator object then needs to define a __next__() method that returns the next element. Python's generators implement this iteration protocol.