Search algorithm - Revision history

50.238.184.138 at 16:18, 10 February 2025

2025-02-10T16:18:43Z

← Previous revision		Revision as of 16:18, 10 February 2025
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	===For sub-structures of a given structure===		===For sub-structures of a given structure===
	The name "combinatorial search" is generally used for algorithms that look for a specific sub-structure of a given [[Discrete mathematics\|discrete structure]], such as a graph, a [[string (computer science)\|string]], a finite [[group (mathematics)\|group]], and so on. The term [[combinatorial optimization]] is typically used when the goal is to find a sub-structure with a maximum (or minimum) value of some parameter. (Since the sub-structure is usually represented in the computer by a set of integer variables with constraints, these problems can be viewed as special cases of constraint satisfaction or discrete optimization; but they are usually formulated and solved in a more abstract setting where the internal representation is not explicitly mentioned.)

	An important and extensively studied subclass are the [[List of algorithms#Graph algorithms\|graph algorithm]]s, in particular [[graph traversal]] algorithms, for finding specific sub-structures in a given graph — such as [[Glossary of graph theory#Subgraphs\|subgraphs]], [[path (graph theory)\|paths]], circuits, and so on. Examples include [[Dijkstra's algorithm]], [[Kruskal's algorithm]], the [[nearest neighbour algorithm]], and [[Prim's algorithm]].		An important and extensively studied subclass are the [[List of algorithms#Graph algorithms\|graph algorithm]]s, in particular [[graph traversal]] algorithms, for finding specific sub-structures in a given graph — such as [[Glossary of graph theory#Subgraphs\|subgraphs]], [[path (graph theory)\|paths]], circuits, and so on. Examples include [[Dijkstra's algorithm]], [[Kruskal's algorithm]], the [[nearest neighbour algorithm]], and [[Prim's algorithm]].

1AmNobody24: Reverted edits by FREE, NATURE, LOVE, TMM (talk) (AV)

2024-12-13T09:17:26Z

Reverted edits by FREE, NATURE, LOVE, TMM (talk) (AV)

← Previous revision		Revision as of 09:17, 13 December 2024
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	Although [[Search engine (computing)\|search engines]] use search algorithms, they belong to the study of [[information retrieval]], not algorithmics.		Although [[Search engine (computing)\|search engines]] use search algorithms, they belong to the study of [[information retrieval]], not algorithmics.

	The appropriate ~~Tinary find~~ algorithm to use often depends on the data structure being searched, and may also include prior knowledge about the data. ~~Find~~ algorithms can be made faster or more efficient by specially constructed database structures, such as [[search tree]]s, [[hash map]]s, and [[database index]]es.{{Sfn\|Beame\|Fich\|2002\|p=39}}{{Sfn\|Knuth\|1998\|loc=§6.5 ("Retrieval on Secondary Keys")}}		The appropriate search algorithm to use 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 tree]]s, [[hash map]]s, and [[database index]]es.{{Sfn\|Beame\|Fich\|2002\|p=39}}{{Sfn\|Knuth\|1998\|loc=§6.5 ("Retrieval on Secondary Keys")}}

	~~Tinary find~~ 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.{{Sfn\|Knuth\|1998\|loc=§6.1 ("Sequential Searching")}} [[Binary search algorithm\|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.{{Sfn\|Knuth\|1998\|loc=§6.2 ("Searching by Comparison of Keys")}} Digital search algorithms work based on the properties of digits in data structures by using numerical keys.{{Sfn\|Knuth\|1998\|loc=§6.3 (Digital Searching)}} Finally, [[Hash table\|hashing]] directly maps keys to records based on a [[hash function]].{{Sfn\|Knuth\|1998\|loc=§6.4, (Hashing)}}		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.{{Sfn\|Knuth\|1998\|loc=§6.1 ("Sequential Searching")}} [[Binary search algorithm\|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.{{Sfn\|Knuth\|1998\|loc=§6.2 ("Searching by Comparison of Keys")}} Digital search algorithms work based on the properties of digits in data structures by using numerical keys.{{Sfn\|Knuth\|1998\|loc=§6.3 (Digital Searching)}} Finally, [[Hash table\|hashing]] directly maps keys to records based on a [[hash function]].{{Sfn\|Knuth\|1998\|loc=§6.4, (Hashing)}}

	Algorithms are often evaluated by their [[computational complexity]], or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of {{math\|''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.		Algorithms are often evaluated by their [[computational complexity]], or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of {{math\|''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.

FREE, NATURE, LOVE, TMM at 09:16, 13 December 2024

2024-12-13T09:16:35Z

← Previous revision		Revision as of 09:16, 13 December 2024
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	Although [[Search engine (computing)\|search engines]] use search algorithms, they belong to the study of [[information retrieval]], not algorithmics.		Although [[Search engine (computing)\|search engines]] use search algorithms, they belong to the study of [[information retrieval]], not algorithmics.

	The appropriate ~~search~~ algorithm to use 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 tree]]s, [[hash map]]s, and [[database index]]es.{{Sfn\|Beame\|Fich\|2002\|p=39}}{{Sfn\|Knuth\|1998\|loc=§6.5 ("Retrieval on Secondary Keys")}}		The appropriate Tinary find algorithm to use often depends on the data structure being searched, and may also include prior knowledge about the data. Find algorithms can be made faster or more efficient by specially constructed database structures, such as [[search tree]]s, [[hash map]]s, and [[database index]]es.{{Sfn\|Beame\|Fich\|2002\|p=39}}{{Sfn\|Knuth\|1998\|loc=§6.5 ("Retrieval on Secondary Keys")}}

	~~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.{{Sfn\|Knuth\|1998\|loc=§6.1 ("Sequential Searching")}} [[Binary search algorithm\|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.{{Sfn\|Knuth\|1998\|loc=§6.2 ("Searching by Comparison of Keys")}} Digital search algorithms work based on the properties of digits in data structures by using numerical keys.{{Sfn\|Knuth\|1998\|loc=§6.3 (Digital Searching)}} Finally, [[Hash table\|hashing]] directly maps keys to records based on a [[hash function]].{{Sfn\|Knuth\|1998\|loc=§6.4, (Hashing)}}		Tinary find 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.{{Sfn\|Knuth\|1998\|loc=§6.1 ("Sequential Searching")}} [[Binary search algorithm\|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.{{Sfn\|Knuth\|1998\|loc=§6.2 ("Searching by Comparison of Keys")}} Digital search algorithms work based on the properties of digits in data structures by using numerical keys.{{Sfn\|Knuth\|1998\|loc=§6.3 (Digital Searching)}} Finally, [[Hash table\|hashing]] directly maps keys to records based on a [[hash function]].{{Sfn\|Knuth\|1998\|loc=§6.4, (Hashing)}}

	Algorithms are often evaluated by their [[computational complexity]], or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of {{math\|''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.		Algorithms are often evaluated by their [[computational complexity]], or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of {{math\|''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.

Tea2min: Reverted 2 edits by Kajalishu (talk)

2024-07-09T07:35:26Z

Reverted 2 edits by Kajalishu (talk)

← Previous revision		Revision as of 07:35, 9 July 2024
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	{{Refbegin}}		{{Refbegin}}
	*[[Wikiversity:Uninformed Search Project\|Uninformed Search Project]] at the [[Wikiversity]].		*[[Wikiversity:Uninformed Search Project\|Uninformed Search Project]] at the [[Wikiversity]].
	{{Refend}}
	{{Refbegin}}
	*[https://syscorp.in/local-seo Best Local SEO Company in Chennai] Syscorp Leading SEO Company
	{{Refend}}		{{Refend}}

Kajalishu: /* External links */

2024-07-09T06:49:43Z

External links

← Previous revision		Revision as of 06:49, 9 July 2024
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	{{Refend}}		{{Refend}}
	{{Refbegin}}		{{Refbegin}}
	*[https://syscorp.in/local-seo Best Local ~~Seo~~ Company in Chennai] Syscorp Leading SEO Company		*[https://syscorp.in/local-seo Best Local SEO Company in Chennai] Syscorp Leading SEO Company
	{{Refend}}		{{Refend}}

Kajalishu: /* External links */

2024-07-09T06:49:09Z

External links

← Previous revision		Revision as of 06:49, 9 July 2024
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	{{Refbegin}}		{{Refbegin}}
	*[[Wikiversity:Uninformed Search Project\|Uninformed Search Project]] at the [[Wikiversity]].		*[[Wikiversity:Uninformed Search Project\|Uninformed Search Project]] at the [[Wikiversity]].
			{{Refend}}
			{{Refbegin}}
			*[https://syscorp.in/local-seo Best Local Seo Company in Chennai] Syscorp Leading SEO Company
	{{Refend}}		{{Refend}}

78.209.97.129: /* Applications of search algorithms */

2024-06-09T12:05:52Z

Applications of search algorithms

← Previous revision		Revision as of 12:05, 9 June 2024
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	* Finding a combination or password from the whole set of possibilities		* Finding a combination or password from the whole set of possibilities
	* [[Factorization\|Factoring]] an integer (an important problem in [[cryptography]])		* [[Factorization\|Factoring]] an integer (an important problem in [[cryptography]])
			* Search engine optimization (SEO) and content optimization for web crawlers
	* Optimizing an industrial process, such as a [[chemical reaction]], by changing the parameters of the process (like temperature, pressure, and pH)		* Optimizing an industrial process, such as a [[chemical reaction]], by changing the parameters of the process (like temperature, pressure, and pH)
	* Retrieving a record from a [[database]]		* Retrieving a record from a [[database]]

Adakiko: Reverted edits by Pawanpatra (talk): addition of unnecessary/inappropriate external links (HG) (3.4.12)

2023-11-03T11:45:21Z

Reverted edits by Pawanpatra (talk): addition of unnecessary/inappropriate external links (HG) (3.4.12)

← Previous revision		Revision as of 11:45, 3 November 2023
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	*{{cite journal\|last1=Beame\|first1=Paul\|last2=Fich\|first2=Faith\|author2-link=Faith Ellen\|title = Optimal Bounds for the Predecessor Problem and Related Problems\|journal=[[Journal of Computer and System Sciences]]\|volume=65\|issue=1\|date=August 2002\|pages=38–72\|doi=10.1006/jcss.2002.1822\|s2cid=1991980 \|doi-access=free}}		*{{cite journal\|last1=Beame\|first1=Paul\|last2=Fich\|first2=Faith\|author2-link=Faith Ellen\|title = Optimal Bounds for the Predecessor Problem and Related Problems\|journal=[[Journal of Computer and System Sciences]]\|volume=65\|issue=1\|date=August 2002\|pages=38–72\|doi=10.1006/jcss.2002.1822\|s2cid=1991980 \|doi-access=free}}
	*{{Cite journal\|last1=Schmittou\|first1=Thomas\|last2=Schmittou\|first2=Faith E.\|date=2002-08-01\|title=Optimal Bounds for the Predecessor Problem and Related Problems\|journal=Journal of Computer and System Sciences\|volume=65\|issue=1\|pages=38–72\|doi=10.1006/jcss.2002.1822\|doi-access=free \|ref=none}}		*{{Cite journal\|last1=Schmittou\|first1=Thomas\|last2=Schmittou\|first2=Faith E.\|date=2002-08-01\|title=Optimal Bounds for the Predecessor Problem and Related Problems\|journal=Journal of Computer and System Sciences\|volume=65\|issue=1\|pages=38–72\|doi=10.1006/jcss.2002.1822\|doi-access=free \|ref=none}}
	*Patel, N. (2023, October 27). What is The Future of Search? Answered by Neil Patel - EMB Blogs. EMB Blogs. https://blog.emb.global/future-of-search/

	==External links==		==External links==

Pawanpatra: /* Articles */

2023-11-02T04:53:41Z

Articles

← Previous revision		Revision as of 04:53, 2 November 2023
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	*{{cite journal\|last1=Beame\|first1=Paul\|last2=Fich\|first2=Faith\|author2-link=Faith Ellen\|title = Optimal Bounds for the Predecessor Problem and Related Problems\|journal=[[Journal of Computer and System Sciences]]\|volume=65\|issue=1\|date=August 2002\|pages=38–72\|doi=10.1006/jcss.2002.1822\|s2cid=1991980 \|doi-access=free}}		*{{cite journal\|last1=Beame\|first1=Paul\|last2=Fich\|first2=Faith\|author2-link=Faith Ellen\|title = Optimal Bounds for the Predecessor Problem and Related Problems\|journal=[[Journal of Computer and System Sciences]]\|volume=65\|issue=1\|date=August 2002\|pages=38–72\|doi=10.1006/jcss.2002.1822\|s2cid=1991980 \|doi-access=free}}
	*{{Cite journal\|last1=Schmittou\|first1=Thomas\|last2=Schmittou\|first2=Faith E.\|date=2002-08-01\|title=Optimal Bounds for the Predecessor Problem and Related Problems\|journal=Journal of Computer and System Sciences\|volume=65\|issue=1\|pages=38–72\|doi=10.1006/jcss.2002.1822\|doi-access=free \|ref=none}}		*{{Cite journal\|last1=Schmittou\|first1=Thomas\|last2=Schmittou\|first2=Faith E.\|date=2002-08-01\|title=Optimal Bounds for the Predecessor Problem and Related Problems\|journal=Journal of Computer and System Sciences\|volume=65\|issue=1\|pages=38–72\|doi=10.1006/jcss.2002.1822\|doi-access=free \|ref=none}}
			*Patel, N. (2023, October 27). What is The Future of Search? Answered by Neil Patel - EMB Blogs. EMB Blogs. https://blog.emb.global/future-of-search/

	==External links==		==External links==

MrOllie: Reverted 1 edit by Pawanpatra (talk): Rv blog spam

2023-11-01T11:12:12Z

Reverted 1 edit by Pawanpatra (talk): Rv blog spam

← Previous revision		Revision as of 11:12, 1 November 2023
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	{{Refend}}		{{Refend}}

	* [https://blog.emb.global/future-of-search/ What is The Future of Search? Answered by Neil Patel] at the [https://blog.emb.global/ EMB Blog]
	{{Algorithmic paradigms}}		{{Algorithmic paradigms}}

← Previous revision		Revision as of 09:17, 13 December 2024
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	Although [[Search engine (computing)\|search engines]] use search algorithms, they belong to the study of [[information retrieval]], not algorithmics.		Although [[Search engine (computing)\|search engines]] use search algorithms, they belong to the study of [[information retrieval]], not algorithmics.

	The appropriate ~~Tinary find~~ algorithm to use often depends on the data structure being searched, and may also include prior knowledge about the data. ~~Find~~ algorithms can be made faster or more efficient by specially constructed database structures, such as [[search tree]]s, [[hash map]]s, and [[database index]]es.{{Sfn\|Beame\|Fich\|2002\|p=39}}{{Sfn\|Knuth\|1998\|loc=§6.5 ("Retrieval on Secondary Keys")}}		The appropriate search algorithm to use 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 tree]]s, [[hash map]]s, and [[database index]]es.{{Sfn\|Beame\|Fich\|2002\|p=39}}{{Sfn\|Knuth\|1998\|loc=§6.5 ("Retrieval on Secondary Keys")}}

	~~Tinary find~~ 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.{{Sfn\|Knuth\|1998\|loc=§6.1 ("Sequential Searching")}} [[Binary search algorithm\|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.{{Sfn\|Knuth\|1998\|loc=§6.2 ("Searching by Comparison of Keys")}} Digital search algorithms work based on the properties of digits in data structures by using numerical keys.{{Sfn\|Knuth\|1998\|loc=§6.3 (Digital Searching)}} Finally, [[Hash table\|hashing]] directly maps keys to records based on a [[hash function]].{{Sfn\|Knuth\|1998\|loc=§6.4, (Hashing)}}		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.{{Sfn\|Knuth\|1998\|loc=§6.1 ("Sequential Searching")}} [[Binary search algorithm\|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.{{Sfn\|Knuth\|1998\|loc=§6.2 ("Searching by Comparison of Keys")}} Digital search algorithms work based on the properties of digits in data structures by using numerical keys.{{Sfn\|Knuth\|1998\|loc=§6.3 (Digital Searching)}} Finally, [[Hash table\|hashing]] directly maps keys to records based on a [[hash function]].{{Sfn\|Knuth\|1998\|loc=§6.4, (Hashing)}}

	Algorithms are often evaluated by their [[computational complexity]], or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of {{math\|''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.		Algorithms are often evaluated by their [[computational complexity]], or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of {{math\|''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.

← Previous revision		Revision as of 09:16, 13 December 2024
Line 9:		Line 9:
	Although [[Search engine (computing)\|search engines]] use search algorithms, they belong to the study of [[information retrieval]], not algorithmics.		Although [[Search engine (computing)\|search engines]] use search algorithms, they belong to the study of [[information retrieval]], not algorithmics.

	The appropriate ~~search~~ algorithm to use 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 tree]]s, [[hash map]]s, and [[database index]]es.{{Sfn\|Beame\|Fich\|2002\|p=39}}{{Sfn\|Knuth\|1998\|loc=§6.5 ("Retrieval on Secondary Keys")}}		The appropriate Tinary find algorithm to use often depends on the data structure being searched, and may also include prior knowledge about the data. Find algorithms can be made faster or more efficient by specially constructed database structures, such as [[search tree]]s, [[hash map]]s, and [[database index]]es.{{Sfn\|Beame\|Fich\|2002\|p=39}}{{Sfn\|Knuth\|1998\|loc=§6.5 ("Retrieval on Secondary Keys")}}

	~~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.{{Sfn\|Knuth\|1998\|loc=§6.1 ("Sequential Searching")}} [[Binary search algorithm\|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.{{Sfn\|Knuth\|1998\|loc=§6.2 ("Searching by Comparison of Keys")}} Digital search algorithms work based on the properties of digits in data structures by using numerical keys.{{Sfn\|Knuth\|1998\|loc=§6.3 (Digital Searching)}} Finally, [[Hash table\|hashing]] directly maps keys to records based on a [[hash function]].{{Sfn\|Knuth\|1998\|loc=§6.4, (Hashing)}}		Tinary find 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.{{Sfn\|Knuth\|1998\|loc=§6.1 ("Sequential Searching")}} [[Binary search algorithm\|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.{{Sfn\|Knuth\|1998\|loc=§6.2 ("Searching by Comparison of Keys")}} Digital search algorithms work based on the properties of digits in data structures by using numerical keys.{{Sfn\|Knuth\|1998\|loc=§6.3 (Digital Searching)}} Finally, [[Hash table\|hashing]] directly maps keys to records based on a [[hash function]].{{Sfn\|Knuth\|1998\|loc=§6.4, (Hashing)}}

	Algorithms are often evaluated by their [[computational complexity]], or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of {{math\|''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.		Algorithms are often evaluated by their [[computational complexity]], or maximum theoretical run time. Binary search functions, for example, have a maximum complexity of {{math\|''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.