Timeline of algorithms - Revision history

MorningSciFi: /* 1980s */ formatting consistency

2025-05-13T00:53:04Z

1980s: formatting consistency

MorningSciFi: /* 1950s */ Add Hamming codes

2025-05-13T00:51:46Z

1950s: Add Hamming codes

← Previous revision		Revision as of 00:51, 13 May 2025
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	==1950s==		==1950s==
			* 1950 – [[Hamming code\|Hamming codes]] developed by [[Richard Hamming]]
	* 1952 – [[Huffman coding]] developed by [[David A. Huffman]]		* 1952 – [[Huffman coding]] developed by [[David A. Huffman]]
	* 1953 – [[Simulated annealing]] introduced by [[Nicholas Metropolis]]		* 1953 – [[Simulated annealing]] introduced by [[Nicholas Metropolis]]

Tassedethe: v2.05 - Repaired 1 link to disambiguation page - (You can help) - Michael Burrows

2025-03-02T17:34:28Z

v2.05 - Repaired 1 link to disambiguation page - (You can help) - Michael Burrows

← Previous revision		Revision as of 17:34, 2 March 2025
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	* 1993 – [[Karger's algorithm]] to compute the minimum cut of a connected graph by David Karger		* 1993 – [[Karger's algorithm]] to compute the minimum cut of a connected graph by David Karger
	* 1994 – [[Shor's algorithm]] developed by [[Peter Shor]]		* 1994 – [[Shor's algorithm]] developed by [[Peter Shor]]
	* 1994 – [[Burrows–Wheeler transform]] developed by [[Michael Burrows]] and [[David Wheeler (computer scientist)\|David Wheeler]]		* 1994 – [[Burrows–Wheeler transform]] developed by [[Michael Burrows (computer scientist)\|Michael Burrows]] and [[David Wheeler (computer scientist)\|David Wheeler]]
	* 1994 – [[Bootstrap aggregating]] (bagging) developed by [[Leo Breiman]]		* 1994 – [[Bootstrap aggregating]] (bagging) developed by [[Leo Breiman]]
	* 1995 – [[AdaBoost]] algorithm, the first practical boosting algorithm, was introduced by [[Yoav Freund]] and [[Robert Schapire]]		* 1995 – [[AdaBoost]] algorithm, the first practical boosting algorithm, was introduced by [[Yoav Freund]] and [[Robert Schapire]]

MrOllie: unreliable source

2025-02-11T22:08:35Z

unreliable source

← Previous revision		Revision as of 22:08, 11 February 2025
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	==2010s==		==2010s==
	* 2013 – [[Raft (computer science)\|Raft consensus protocol]] published by [[Diego Ongaro]] and [[John Ousterhout]]		* 2013 – [[Raft (computer science)\|Raft consensus protocol]] published by [[Diego Ongaro]] and [[John Ousterhout]]
	* 2015 – YOLO (“[[You Only Look Once]]”) is an effective real-time object recognition algorithm, first described by [[Joseph Redmon]] et al.<ref>{{Cite web \|date=19 December 2023 \|title=YOLO: Real-Time Object Detection \|url=https://pjreddie.com/darknet/yolo/ \|access-date=19 December 2023 \|archive-url=https://web.archive.org/web/20231219001012/https://pjreddie.com/darknet/yolo/ \|archive-date=19 December 2023 }}</ref><ref>{{Cite web \|date=19 December 2023 \|title=Understanding a Real-Time Object Detection Network: You Only Look Once (YOLOv1) \|url=https://pyimagesearch.com/2022/04/11/understanding-a-real-time-object-detection-network-you-only-look-once-yolov1/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220194202/https://pyimagesearch.com/2022/04/11/understanding-a-real-time-object-detection-network-you-only-look-once-yolov1/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=20 December 2023 \|title=how to use darknet to train your own neural network \|url=https://www.surfactants.net/how-to-use-darknet-to-train-your-own-neural-network/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220203408/https://www.surfactants.net/how-to-use-darknet-to-train-your-own-neural-network/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=20 December 2023 \|title=How computers learn to recognize objects instantly \|url=https://www.ted.com/talks/joseph_redmon_how_computers_learn_to_recognize_objects_instantly \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220204643/https://www.ted.com/talks/joseph_redmon_how_computers_learn_to_recognize_objects_instantly \|archive-date=20 December 2023 }}</ref><ref name="Dr Kumar Gaurav - Amit Doegar 2021 1" >{{Cite web \|date=20 December 2023 \|title=Darknet: The Open Source Framework for Deep Neural Networks \|url=https://www.opensourceforu.com/2021/04/darknet-the-open-source-framework-for-deep-neural-networks/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220210732/https://www.opensourceforu.com/2021/04/darknet-the-open-source-framework-for-deep-neural-networks/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=21 December 2023 \|title=Your Comprehensive Guide to the YOLO Family of Models \|url=https://blog.roboflow.com/guide-to-yolo-models/ \|access-date=21 December 2023 \|archive-url=https://web.archive.org/web/20231221154639/https://blog.roboflow.com/guide-to-yolo-models/ \|archive-date=21 December 2023 }}</ref>		* 2015 – YOLO (“[[You Only Look Once]]”) is an effective real-time object recognition algorithm, first described by [[Joseph Redmon]] et al.<ref>{{Cite web \|date=19 December 2023 \|title=YOLO: Real-Time Object Detection \|url=https://pjreddie.com/darknet/yolo/ \|access-date=19 December 2023 \|archive-url=https://web.archive.org/web/20231219001012/https://pjreddie.com/darknet/yolo/ \|archive-date=19 December 2023 }}</ref><ref>{{Cite web \|date=19 December 2023 \|title=Understanding a Real-Time Object Detection Network: You Only Look Once (YOLOv1) \|url=https://pyimagesearch.com/2022/04/11/understanding-a-real-time-object-detection-network-you-only-look-once-yolov1/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220194202/https://pyimagesearch.com/2022/04/11/understanding-a-real-time-object-detection-network-you-only-look-once-yolov1/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=20 December 2023 \|title=how to use darknet to train your own neural network \|url=https://www.surfactants.net/how-to-use-darknet-to-train-your-own-neural-network/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220203408/https://www.surfactants.net/how-to-use-darknet-to-train-your-own-neural-network/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=20 December 2023 \|title=How computers learn to recognize objects instantly \|url=https://www.ted.com/talks/joseph_redmon_how_computers_learn_to_recognize_objects_instantly \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220204643/https://www.ted.com/talks/joseph_redmon_how_computers_learn_to_recognize_objects_instantly \|archive-date=20 December 2023 }}</ref><ref name="Dr Kumar Gaurav - Amit Doegar 2021 1" >{{Cite web \|date=20 December 2023 \|title=Darknet: The Open Source Framework for Deep Neural Networks \|url=https://www.opensourceforu.com/2021/04/darknet-the-open-source-framework-for-deep-neural-networks/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220210732/https://www.opensourceforu.com/2021/04/darknet-the-open-source-framework-for-deep-neural-networks/ \|archive-date=20 December 2023 }}</ref>

	==References==		==References==

Pgmcr42: removed nonsensical paragraph

2025-02-09T14:45:29Z

removed nonsensical paragraph

← Previous revision		Revision as of 14:45, 9 February 2025
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	The following '''timeline of algorithms''' outlines the development of [[algorithm]]s (mainly "mathematical recipes") since their inception.		The following '''timeline of algorithms''' outlines the development of [[algorithm]]s (mainly "mathematical recipes") since their inception.
	The timeline of algorithm is the causality by viralityies fractals of inelementation of dysplots dysconstituate on the inversation of structuration under centromother by the trigopolynomate on must unplannings by euclidians substractive dysdimensiations on tree dysaxablinging wheilth of centrotimes and centrofielding directionnabilatnings and by the jouissivs strongedablings of falsivs dysanalisablessing sigmazetaeds irreal dysélémentation, the unrealised by dysanglets of unlacunateds makes the takers realms forgottenningers, the emptyvityies by variabilityies by under angletsations and the dysploteds emptyers by black plots holesins, the dig acomplementation of afractalityies, the timelines of algorithms by euclidians lissage of goodies e by rerévoluated mediumityies factorisms on the maximals iconics destroyers untemporits, the tentation of best emptyied no durated unphysicated, the constrative of unfustratives voice unavancement, the timeline of algorithm like unaccess for all termings of unexisted worts bests volontaritable...

	==Antiquity==		==Antiquity==

Izno: Reverted edit by Polished Prose (talk) to last version by 2A01:CB09:E060:4167:BD6B:21C8:153:762C

2025-02-02T19:59:58Z

Reverted edit by Polished Prose (talk) to last version by 2A01:CB09:E060:4167:BD6B:21C8:153:762C

← Previous revision		Revision as of 19:59, 2 February 2025
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	The following '''timeline of algorithms''' outlines the development of [[algorithm]]s (mainly "mathematical recipes") since their inception.		The following '''timeline of algorithms''' outlines the development of [[algorithm]]s (mainly "mathematical recipes") since their inception.
			The timeline of algorithm is the causality by viralityies fractals of inelementation of dysplots dysconstituate on the inversation of structuration under centromother by the trigopolynomate on must unplannings by euclidians substractive dysdimensiations on tree dysaxablinging wheilth of centrotimes and centrofielding directionnabilatnings and by the jouissivs strongedablings of falsivs dysanalisablessing sigmazetaeds irreal dysélémentation, the unrealised by dysanglets of unlacunateds makes the takers realms forgottenningers, the emptyvityies by variabilityies by under angletsations and the dysploteds emptyers by black plots holesins, the dig acomplementation of afractalityies, the timelines of algorithms by euclidians lissage of goodies e by rerévoluated mediumityies factorisms on the maximals iconics destroyers untemporits, the tentation of best emptyied no durated unphysicated, the constrative of unfustratives voice unavancement, the timeline of algorithm like unaccess for all termings of unexisted worts bests volontaritable...

	==Antiquity==		==Antiquity==

Polished Prose: Looks like gobbledygook

2025-02-01T15:45:14Z

Looks like gobbledygook

← Previous revision		Revision as of 15:45, 1 February 2025
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	The following '''timeline of algorithms''' outlines the development of [[algorithm]]s (mainly "mathematical recipes") since their inception.		The following '''timeline of algorithms''' outlines the development of [[algorithm]]s (mainly "mathematical recipes") since their inception.
	The timeline of algorithm is the causality by viralityies fractals of inelementation of dysplots dysconstituate on the inversation of structuration under centromother by the trigopolynomate on must unplannings by euclidians substractive dysdimensiations on tree dysaxablinging wheilth of centrotimes and centrofielding directionnabilatnings and by the jouissivs strongedablings of falsivs dysanalisablessing sigmazetaeds irreal dysélémentation, the unrealised by dysanglets of unlacunateds makes the takers realms forgottenningers, the emptyvityies by variabilityies by under angletsations and the dysploteds emptyers by black plots holesins, the dig acomplementation of afractalityies, the timelines of algorithms by euclidians lissage of goodies e by rerévoluated mediumityies factorisms on the maximals iconics destroyers untemporits, the tentation of best emptyied no durated unphysicated, the constrative of unfustratives voice unavancement, the timeline of algorithm like unaccess for all termings of unexisted worts bests volontaritable...

	==Antiquity==		==Antiquity==

2A01:CB09:E060:4167:BD6B:21C8:153:762C: https://www.bibicreationgeantvertconseil.wordpress.com

2025-02-01T15:42:17Z

https://www.bibicreationgeantvertconseil.wordpress.com

← Previous revision		Revision as of 15:42, 1 February 2025
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	The following '''timeline of algorithms''' outlines the development of [[algorithm]]s (mainly "mathematical recipes") since their inception.		The following '''timeline of algorithms''' outlines the development of [[algorithm]]s (mainly "mathematical recipes") since their inception.
			The timeline of algorithm is the causality by viralityies fractals of inelementation of dysplots dysconstituate on the inversation of structuration under centromother by the trigopolynomate on must unplannings by euclidians substractive dysdimensiations on tree dysaxablinging wheilth of centrotimes and centrofielding directionnabilatnings and by the jouissivs strongedablings of falsivs dysanalisablessing sigmazetaeds irreal dysélémentation, the unrealised by dysanglets of unlacunateds makes the takers realms forgottenningers, the emptyvityies by variabilityies by under angletsations and the dysploteds emptyers by black plots holesins, the dig acomplementation of afractalityies, the timelines of algorithms by euclidians lissage of goodies e by rerévoluated mediumityies factorisms on the maximals iconics destroyers untemporits, the tentation of best emptyied no durated unphysicated, the constrative of unfustratives voice unavancement, the timeline of algorithm like unaccess for all termings of unexisted worts bests volontaritable...

	==Antiquity==		==Antiquity==

Tc14Hd: 2000 BC is not medieval

2024-09-18T16:40:11Z

2000 BC is not medieval

← Previous revision		Revision as of 16:40, 18 September 2024
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	The following '''timeline of algorithms''' outlines the development of [[algorithm]]s (mainly "mathematical recipes") since their inception.		The following '''timeline of algorithms''' outlines the development of [[algorithm]]s (mainly "mathematical recipes") since their inception.

	==~~Medieval Period~~==		==Antiquity==
	* Before – [[writing]] about "[[recipes]]" (on cooking, ~~rituals~~, agriculture and other themes)		* Before – [[writing]] about "[[recipes]]" (on [[cooking]], [[ritual]]s, [[agriculture]] and other themes)
	* c. 1700–2000 BC – Egyptians develop earliest known algorithms for [[Ancient Egyptian multiplication\|multiplying]] two numbers		* c. 1700–2000 BC – Egyptians develop earliest known algorithms for [[Ancient Egyptian multiplication\|multiplying]] two numbers
	* c. 1600 BC – [[Babylonia]]ns develop earliest known algorithms for [[factorization]] and finding [[square root]]s		* c. 1600 BC – [[Babylonia]]ns develop earliest known algorithms for [[factorization]] and finding [[square root]]s
Line 10:		Line 10:
	* c. 200 BC – the [[Sieve of Eratosthenes]]		* c. 200 BC – the [[Sieve of Eratosthenes]]
	* 263 AD – [[Gaussian elimination]] described by [[Liu Hui]]		* 263 AD – [[Gaussian elimination]] described by [[Liu Hui]]

			==Medieval Period==
	* 628 – [[Chakravala method]] described by [[Brahmagupta]]		* 628 – [[Chakravala method]] described by [[Brahmagupta]]
	* c. 820 – [[Al-Khawarizmi]] described algorithms for solving [[linear equation]]s and [[quadratic equation]]s in his ''[[The Compendious Book on Calculation by Completion and Balancing\|Algebra]]''; the word ''algorithm'' comes from his name		* c. 820 – [[Al-Khawarizmi]] described algorithms for solving [[linear equation]]s and [[quadratic equation]]s in his ''[[The Compendious Book on Calculation by Completion and Balancing\|Algebra]]''; the word ''algorithm'' comes from his name

Cosmia Nebula: /* 2010s */ link

2024-09-15T05:17:51Z

2010s: link

← Previous revision		Revision as of 05:17, 15 September 2024
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	==2010s==		==2010s==
	* 2013 – [[Raft (computer science)\|Raft consensus protocol]] published by [[Diego Ongaro]] and [[John Ousterhout]]		* 2013 – [[Raft (computer science)\|Raft consensus protocol]] published by [[Diego Ongaro]] and [[John Ousterhout]]
	* 2015 – [[YOLO (~~algorithm)\|YOLO]] (“You~~ Only Look ~~Once”~~) is an effective real-time object recognition algorithm, first described by [[Joseph Redmon]] et al.<ref>{{Cite web \|date=19 December 2023 \|title=YOLO: Real-Time Object Detection \|url=https://pjreddie.com/darknet/yolo/ \|access-date=19 December 2023 \|archive-url=https://web.archive.org/web/20231219001012/https://pjreddie.com/darknet/yolo/ \|archive-date=19 December 2023 }}</ref><ref>{{Cite web \|date=19 December 2023 \|title=Understanding a Real-Time Object Detection Network: You Only Look Once (YOLOv1) \|url=https://pyimagesearch.com/2022/04/11/understanding-a-real-time-object-detection-network-you-only-look-once-yolov1/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220194202/https://pyimagesearch.com/2022/04/11/understanding-a-real-time-object-detection-network-you-only-look-once-yolov1/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=20 December 2023 \|title=how to use darknet to train your own neural network \|url=https://www.surfactants.net/how-to-use-darknet-to-train-your-own-neural-network/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220203408/https://www.surfactants.net/how-to-use-darknet-to-train-your-own-neural-network/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=20 December 2023 \|title=How computers learn to recognize objects instantly \|url=https://www.ted.com/talks/joseph_redmon_how_computers_learn_to_recognize_objects_instantly \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220204643/https://www.ted.com/talks/joseph_redmon_how_computers_learn_to_recognize_objects_instantly \|archive-date=20 December 2023 }}</ref><ref name="Dr Kumar Gaurav - Amit Doegar 2021 1" >{{Cite web \|date=20 December 2023 \|title=Darknet: The Open Source Framework for Deep Neural Networks \|url=https://www.opensourceforu.com/2021/04/darknet-the-open-source-framework-for-deep-neural-networks/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220210732/https://www.opensourceforu.com/2021/04/darknet-the-open-source-framework-for-deep-neural-networks/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=21 December 2023 \|title=Your Comprehensive Guide to the YOLO Family of Models \|url=https://blog.roboflow.com/guide-to-yolo-models/ \|access-date=21 December 2023 \|archive-url=https://web.archive.org/web/20231221154639/https://blog.roboflow.com/guide-to-yolo-models/ \|archive-date=21 December 2023 }}</ref>		* 2015 – YOLO (“[[You Only Look Once]]”) is an effective real-time object recognition algorithm, first described by [[Joseph Redmon]] et al.<ref>{{Cite web \|date=19 December 2023 \|title=YOLO: Real-Time Object Detection \|url=https://pjreddie.com/darknet/yolo/ \|access-date=19 December 2023 \|archive-url=https://web.archive.org/web/20231219001012/https://pjreddie.com/darknet/yolo/ \|archive-date=19 December 2023 }}</ref><ref>{{Cite web \|date=19 December 2023 \|title=Understanding a Real-Time Object Detection Network: You Only Look Once (YOLOv1) \|url=https://pyimagesearch.com/2022/04/11/understanding-a-real-time-object-detection-network-you-only-look-once-yolov1/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220194202/https://pyimagesearch.com/2022/04/11/understanding-a-real-time-object-detection-network-you-only-look-once-yolov1/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=20 December 2023 \|title=how to use darknet to train your own neural network \|url=https://www.surfactants.net/how-to-use-darknet-to-train-your-own-neural-network/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220203408/https://www.surfactants.net/how-to-use-darknet-to-train-your-own-neural-network/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=20 December 2023 \|title=How computers learn to recognize objects instantly \|url=https://www.ted.com/talks/joseph_redmon_how_computers_learn_to_recognize_objects_instantly \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220204643/https://www.ted.com/talks/joseph_redmon_how_computers_learn_to_recognize_objects_instantly \|archive-date=20 December 2023 }}</ref><ref name="Dr Kumar Gaurav - Amit Doegar 2021 1" >{{Cite web \|date=20 December 2023 \|title=Darknet: The Open Source Framework for Deep Neural Networks \|url=https://www.opensourceforu.com/2021/04/darknet-the-open-source-framework-for-deep-neural-networks/ \|access-date=20 December 2023 \|archive-url=https://web.archive.org/web/20231220210732/https://www.opensourceforu.com/2021/04/darknet-the-open-source-framework-for-deep-neural-networks/ \|archive-date=20 December 2023 }}</ref><ref>{{Cite web \|date=21 December 2023 \|title=Your Comprehensive Guide to the YOLO Family of Models \|url=https://blog.roboflow.com/guide-to-yolo-models/ \|access-date=21 December 2023 \|archive-url=https://web.archive.org/web/20231221154639/https://blog.roboflow.com/guide-to-yolo-models/ \|archive-date=21 December 2023 }}</ref>

	==References==		==References==

← Previous revision		Revision as of 00:53, 13 May 2025
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	* 1984 – [[Lempel–Ziv–Welch\|LZW]] algorithm developed from [[LZ78]] by [[Terry Welch]]		* 1984 – [[Lempel–Ziv–Welch\|LZW]] algorithm developed from [[LZ78]] by [[Terry Welch]]
	* 1984 – [[Karmarkar's interior-point algorithm]] developed by [[Narendra Karmarkar]]		* 1984 – [[Karmarkar's interior-point algorithm]] developed by [[Narendra Karmarkar]]
	* 1984 - [[ACORN PRNG]] discovered by Roy Wikramaratna and used privately		* 1984 – [[ACORN PRNG]] discovered by Roy Wikramaratna and used privately
	* 1985 – [[Simulated annealing]] independently developed by [[V. Cerny]]		* 1985 – [[Simulated annealing]] independently developed by [[V. Cerny]]
	* 1985 - [[Car–Parrinello molecular dynamics]] developed by [[Roberto Car]] and [[Michele Parrinello]]		* 1985 – [[Car–Parrinello molecular dynamics]] developed by [[Roberto Car]] and [[Michele Parrinello]]
	* 1985 – [[Splay tree]]s discovered by [[Daniel Dominic Sleator\|Sleator]] and [[Robert Endre Tarjan\|Tarjan]]		* 1985 – [[Splay tree]]s discovered by [[Daniel Dominic Sleator\|Sleator]] and [[Robert Endre Tarjan\|Tarjan]]
	* 1986 – [[Blum Blum Shub]] proposed by [[Lenore Blum\|L. Blum]], [[Manuel Blum\|M. Blum]], and [[Michael Shub\|M. Shub]]		* 1986 – [[Blum Blum Shub]] proposed by [[Lenore Blum\|L. Blum]], [[Manuel Blum\|M. Blum]], and [[Michael Shub\|M. Shub]]
	* 1986 – [[Push–relabel maximum flow algorithm\|Push relabel maximum flow algorithm]] by Andrew Goldberg and Robert Tarjan		* 1986 – [[Push–relabel maximum flow algorithm\|Push relabel maximum flow algorithm]] by Andrew Goldberg and Robert Tarjan
	* 1986 - [[Barnes–Hut simulation\|Barnes–Hut tree method]] developed by [[Josh Barnes]] and [[Piet Hut]] for fast approximate simulation of [[n-body problem]]s		* 1986 – [[Barnes–Hut simulation\|Barnes–Hut tree method]] developed by [[Josh Barnes]] and [[Piet Hut]] for fast approximate simulation of [[n-body problem]]s
	* 1987 – [[Fast multipole method]] developed by [[Leslie Greengard]] and [[Vladimir Rokhlin (American scientist)\|Vladimir Rokhlin]]		* 1987 – [[Fast multipole method]] developed by [[Leslie Greengard]] and [[Vladimir Rokhlin (American scientist)\|Vladimir Rokhlin]]
	* 1988 – [[Special number field sieve]] developed by [[John Pollard (mathematician)\|John Pollard]]		* 1988 – [[Special number field sieve]] developed by [[John Pollard (mathematician)\|John Pollard]]
	* 1989 - [[ACORN PRNG]] published by Roy Wikramaratna		* 1989 – [[ACORN PRNG]] published by Roy Wikramaratna
	* 1989 – [[Paxos (computer science)\|Paxos protocol]] developed by [[Leslie Lamport]]		* 1989 – [[Paxos (computer science)\|Paxos protocol]] developed by [[Leslie Lamport]]
	* 1989 – [[Skip list]] discovered by [[William Pugh (computer_scientist)\|William Pugh]]		* 1989 – [[Skip list]] discovered by [[William Pugh (computer_scientist)\|William Pugh]]