search algorithm - определение. Что такое search algorithm
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  3. search algorithm

Что (кто) такое search algorithm - определение

ANY ALGORITHM WHICH SOLVES THE SEARCH PROBLEM, NAMELY, TO RETRIEVE INFORMATION STORED WITHIN SOME DATA STRUCTURE, OR CALCULATED IN THE SEARCH SPACE OF A PROBLEM DOMAIN, EITHER WITH DISCRETE OR CONTINUOUS VALUES
Search algorithms; Search Algorithm; Informed search algorithm; Search Algorithms; Searching algorithms; Uninformed search algorithm; Informed search; Keyword Search Method; Searching algorithm; Array search; Adversarial search; Search ranking algorithm; Ranking Algorithm; Applications of search algorithms; Quantum search algorithm
  • Visual representation of a [[hash table]], a [[data structure]] that allows for fast retrieval of information.
Найдено результатов: 1685
search algorithm         
<theory> Any algorithm for identifying a solution to a problem (a search problem) out of a space of potential solutions by considering several potential solutions until one is found that meets certain criteria. See A* search, beam search, best-first search, breadth-first search, depth-first search. (2007-11-03)
A* search algorithm         
  • The A* algorithm finding a path of railroads between Washington, D.C. and Los Angeles.
  • Illustration of A* search for finding a path between two points on a graph. From left to right, a heuristic that prefers points closer to the goal is used increasingly.
  • An example of A* algorithm in action (nodes are cities connected with roads, h(x) is the straight-line distance to the target point) Green: Start, Blue: Target, Orange: Visited
  • A* pathfinding algorithm navigating around a randomly-generated maze
  • A* was invented by researchers working on Shakey the Robot's path planning.
ALGORITHM USED FOR PATHFINDING AND GRAPH TRAVERSAL
A Star Search Algorithm; A star search algorithm; A-star algorithm; A-star search algorithm; A* algorithm; A* search; A-star; A Star; A star search; TBA*; New Bidirectional A*
A* (pronounced "A-star") is a graph traversal and path search algorithm, which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. One major practical drawback is its O(b^d) space complexity, as it stores all generated nodes in memory.
https://en.wikipedia.org/wiki/A*_search_algorithm
Binary search algorithm         
  • Binary search can be adapted to compute approximate matches. In the example above, the rank, predecessor, successor, and nearest neighbor are shown for the target value <math>5</math>, which is not in the array.
  • binary-search
  • The worst case is reached when the search reaches the deepest level of the tree, while the best case is reached when the target value is the middle element.
  • tree]] representing binary search. The array being searched here is <math>[20, 30, 40, 50, 80, 90, 100]</math>, and the target value is <math>40</math>.
  • [[Binary search tree]]s are searched using an algorithm similar to binary search.
  • Visualization of [[exponential search]]ing finding the upper bound for the subsequent binary search
  • In [[fractional cascading]], each array has pointers to every second element of another array, so only one binary search has to be performed to search all the arrays.
  • Visualization of [[interpolation search]] using linear interpolation. In this case, no searching is needed because the estimate of the target's location within the array is correct. Other implementations may specify another function for estimating the target's location.
  • In noisy binary search, there is a certain probability that a comparison is incorrect.
  • [[Uniform binary search]] stores the difference between the current and the two next possible middle elements instead of specific bounds.
SEARCH ALGORITHM IN SORTED LISTS THAT OPERATES BY DECREASING THE SEARCH SPACE BY HALF EACH PASS
Binary chop; Binary search; Bsearch; Binary Search; Half-interval search; Half-interval search method; Half interval search method
In computer science, binary search, also known as half-interval search, logarithmic search, or binary chop, is a search algorithm that finds the position of a target value within a sorted array. Binary search compares the target value to the middle element of the array.
https://en.wikipedia.org/wiki/Binary_search_algorithm
A* search         
  • The A* algorithm finding a path of railroads between Washington, D.C. and Los Angeles.
  • Illustration of A* search for finding a path between two points on a graph. From left to right, a heuristic that prefers points closer to the goal is used increasingly.
  • An example of A* algorithm in action (nodes are cities connected with roads, h(x) is the straight-line distance to the target point) Green: Start, Blue: Target, Orange: Visited
  • A* pathfinding algorithm navigating around a randomly-generated maze
  • A* was invented by researchers working on Shakey the Robot's path planning.
ALGORITHM USED FOR PATHFINDING AND GRAPH TRAVERSAL
A Star Search Algorithm; A star search algorithm; A-star algorithm; A-star search algorithm; A* algorithm; A* search; A-star; A Star; A star search; TBA*; New Bidirectional A*
<algorithm> A graph search algorithm. A* is guaranteed to find a minimal solution path before any other solution paths, if a solution exists, in other words, it is an "admissible" search algorithm. Each path is assigned a value based on the cost of the path (e.g. its length) and an (under)estimate of the cost of completing the path, i.e. the cost of a path from the end of the current path to a solution. (1995-03-31)
Grover's algorithm         
  • [[Quantum circuit]] representation of Grover's algorithm
  • \omega\rang</math> as shown.
ALGORITHM
Quadratic speedup theorem; Grover algorithm; Grovers algorithm; Grover search algorithm; Quantum partial search; Quantum oracle
In quantum computing, Grover's algorithm, also known as the quantum search algorithm, refers to a quantum algorithm for unstructured search that finds with high probability the unique input to a black box function that produces a particular output value, using just O(\sqrt{N}) evaluations of the function, where N is the size of the function's domain. It was devised by Lov Grover in 1996.
https://en.wikipedia.org/wiki/Grover's_algorithm
String-searching algorithm         
ALGORITHM WHICH SEARCHES FOR PATTERNS WITHIN STRINGS
String search algorithms; String search algorithm; String searching; String matching; String search; Naive string search; Text searching; Substring search; Deterministic finite automata string search; Text match; Exact string matching; String searching algorithms; String matching algorithms; String matching algorithm; Search string; String searching algorithm; Text search; Pattern matching in strings
In computer science, string-searching algorithms, sometimes called string-matching algorithms, are an important class of string algorithms that try to find a place where one or several strings (also called patterns) are found within a larger string or text.
https://en.wikipedia.org/wiki/String-searching_algorithm
Ternary search         
TECHNIQUE IN COMPUTER SCIENCE FOR FINDING THE MINIMUM OR MAXIMUM OF A UNIMODAL FUNCTION
Trinary search; Ternary Search
A ternary search algorithm is a technique in computer science for finding the minimum or maximum of a unimodal function. A ternary search determines either that the minimum or maximum cannot be in the first third of the domain or that it cannot be in the last third of the domain, then repeats on the remaining two thirds.
https://en.wikipedia.org/wiki/Ternary_search
Exponential search         
ALGORITHM FOR SEARCHING SORTED, INFINITE LISTS
User:Visovari/sandbox; Wikipedia talk:Articles for creation/Exponential Search; Exponential search algorithm
In computer science, an exponential search (also called doubling search or galloping search or Struzik search) is an algorithm, created by Jon Bentley and Andrew Chi-Chih Yao in 1976, for searching sorted, unbounded/infinite lists. There are numerous ways to implement this with the most common being to determine a range that the search key resides in and performing a binary search within that range.
https://en.wikipedia.org/wiki/Exponential_search
binary search         
  • Binary search can be adapted to compute approximate matches. In the example above, the rank, predecessor, successor, and nearest neighbor are shown for the target value <math>5</math>, which is not in the array.
  • binary-search
  • The worst case is reached when the search reaches the deepest level of the tree, while the best case is reached when the target value is the middle element.
  • tree]] representing binary search. The array being searched here is <math>[20, 30, 40, 50, 80, 90, 100]</math>, and the target value is <math>40</math>.
  • [[Binary search tree]]s are searched using an algorithm similar to binary search.
  • Visualization of [[exponential search]]ing finding the upper bound for the subsequent binary search
  • In [[fractional cascading]], each array has pointers to every second element of another array, so only one binary search has to be performed to search all the arrays.
  • Visualization of [[interpolation search]] using linear interpolation. In this case, no searching is needed because the estimate of the target's location within the array is correct. Other implementations may specify another function for estimating the target's location.
  • In noisy binary search, there is a certain probability that a comparison is incorrect.
  • [[Uniform binary search]] stores the difference between the current and the two next possible middle elements instead of specific bounds.
SEARCH ALGORITHM IN SORTED LISTS THAT OPERATES BY DECREASING THE SEARCH SPACE BY HALF EACH PASS
Binary chop; Binary search; Bsearch; Binary Search; Half-interval search; Half-interval search method; Half interval search method
<algorithm> A search algorithm which repeatedly divides an ordered search space in half according to how the required (key) value compares with the middle element. The following pseudo-C routine performs a binary search return the index of the element of vector "thing[first..last]" equal to "target": if (target < thing[first] || target > thing[last]) return NOT_FOUND; while (first < last) { mid = (first+last)/2; /* truncate to integer */ if (target == thing[mid]) return mid; if (target < thing[mid]) last = mid-1; else first = mid+1; } if (target == thing[last]) return last; return NOT_FOUND; (2003-01-14)
Line search         
OPTIMIZATION ALGORITHM
Line search method; Linesearch method; Linesearch methods; Line-search; Linesearch; Step-length algorithm
In optimization, the line search strategy is one of two basic iterative approaches to find a local minimum \mathbf{x}^* of an objective function f:\mathbb R^n\to\mathbb R. The other approach is trust region.
https://en.wikipedia.org/wiki/Line_search

Википедия

Search algorithm

In computer science, a search algorithm is an algorithm designed to solve a search problem. Search algorithms work to retrieve information stored within particular data structure, or calculated in the search space of a problem domain, with either discrete or continuous values.

Although search engines use search algorithms, they belong to the study of information retrieval, not algorithmics.

The appropriate search algorithm often depends on the data structure being searched, and may also include prior knowledge about the data. Search algorithms can be made faster or more efficient by specially constructed database structures, such as search trees, hash maps, and database indexes.

Search algorithms can be classified based on their mechanism of searching into three types of algorithms: linear, binary, and hashing. Linear search algorithms check every record for the one associated with a target key in a linear fashion. Binary, or half-interval, searches repeatedly target the center of the search structure and divide the search space in half. Comparison search algorithms improve on linear searching by successively eliminating records based on comparisons of the keys until the target record is found, and can be applied on data structures with a defined order. Digital search algorithms work based on the properties of digits in data structures by using numerical keys. Finally, hashing directly maps keys to records based on a hash function.

Algorithms are often evaluated by their computational complexity, or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of O(log n), or logarithmic time. In simple terms, the maximum number of operations needed to find the search target is a logarithmic function of the size of the search space.

https://en.wikipedia.org/wiki/Search algorithm
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