Search results
Results from the WOW.Com Content Network
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 ...
In combinatorial game theory, Lazy SMP is a parallelization technique for tree searching by launching the various depths used in iterative deepening on their respective threads (the technique used by Stockfish [1] [2]).
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.
MTD(f) is an alpha-beta game tree search algorithm modified to use ‘zero-window’ initial search bounds, and memory (usually a transposition table) to reuse intermediate search results. MTD(f) is a shortened form of MTD(n,f) which stands for Memory-enhanced Test Driver with node ‘n’ and value ‘f’. [ 1 ]
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 ...
Typically, the postorder iteration is implemented with the depth-first strategy. Reverse postorder - This is a typical iteration order for forward data-flow problems. In reverse-postorder iteration , a node is visited before any of its successor nodes has been visited, except when the successor is reached by a back edge.
A simplified version of a typical iteration cycle in agile project management. The basic idea behind this method is to develop a system through repeated cycles (iterative) and in smaller portions at a time (incremental), allowing software developers to take advantage of what was learned during development of earlier parts or versions of the system.
On hardware where software pipelining is necessary to improve performance alongside loop unrolling (i.e. hardware which lacks register renaming or implements in-order superscalar execution), additional registers may need to be used to store temporary variables from multiple iterations that could otherwise reuse the same register.