Although specialized data structures designed for fast searching such as hash tables can be searched more efficiently, binary search applies to a wider range of search problems.
42.
Therefore some research of dynamic convex hull algorithms involves the computational complexity of various geometric search problems with convex hulls stored in specific kinds of data structures.
43.
In such search problems, a heuristic can be used to try good choices first so that bad paths can be eliminated early ( see alpha-beta pruning ).
44.
Incremental search algorithms reuse information from previous searches to speed up the current search and solve search problems potentially much faster than solving them repeatedly from scratch.
45.
This discovery and observations led to a great amount of research in 1990 and began research on local search problems and the distinctions between easy and hard problems.
46.
Martin Erwig from Oregon State University has used a variation of the problem to argue for the usability of the Haskell programming language over Prolog for solving search problems.
47.
In particular, fractional cascading speeds up binary searches for the same value in multiple arrays, efficiently solving a series of search problems in computational geometry and numerous other fields.
48.
This is a distinct and different hard problem than the Conjugacy Search Problem ( CSP ) which has been the central hard problem in what is called braid group cryptography.
49.
For example, factoring is a search problem where the instances are ( string representations of ) positive integers and the solutions are ( string representations of ) collections of primes.
50.
The STUCCO contrast set learner treats the task of learning from contrast sets as a tree search problem where the root node of the tree is an empty contrast set.
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