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SMA* or Simplified Memory Bounded A* is a shortest path algorithm based on the A* algorithm. The main advantage of SMA* is that it uses a bounded memory, while the A* algorithm might need exponential memory.
Specific applications of search algorithms include: Problems in combinatorial optimization, such as: . The vehicle routing problem, a form of shortest path problem; The knapsack problem: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as ...
The new egalitarian approach is to rely on memory-bound functions. As stated before, a memory-bound function is a function whose computation time is dominated by the time spent accessing memory. A memory-bound function accesses locations in a large region of memory in an unpredictable way, in such a way that using caches are not effective.
Dijkstra's algorithm, as another example of a uniform-cost search algorithm, can be viewed as a special case of A* where = for all x. [ 12 ] [ 13 ] General depth-first search can be implemented using A* by considering that there is a global counter C initialized with a very large value.
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.
a depth-first search starting at A, assuming that the left edges in the shown graph are chosen before right edges, and assuming the search remembers previously-visited nodes and will not repeat them (since this is a small graph), will visit the nodes in the following order: A, B, D, F, E, C, G.
Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. [1] The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes.
The memory-bounded speedup model is the first work to reveal that memory is the performance constraint for high-end computing and presents a quantitative mathematical formulation for the trade-off between memory and computing. It is based on the memory-bounded function,W=G(n), where W is the work and thus also the computation for most applications.