Algorithm

Key people

• Éva TardosHungarian mathematician
• Joseph KruskalAmerican statistician (1928-2010)
• David S. JohnsonAmerican computer scientist (1945-2016)
• Teuvo KohonenFinnish computer scientist (1934–2021)

Timeline

• 2500 BC2500 BC describes the earliest division algorithm.
• 1600 BC1600 BC, Babylonian clay tablets described algorithms for computing formulas.
• 300 BC300 BC).Examples of ancient Indian mathematics included the Shulba Sutras, the Kerala School, and the Brāhmasphuṭasiddhānta.
• 1225The word algorism in English came to mean the use of place-value notation in calculations; it occurs in the Ancrene Wisse from circa 1225.
• 1596By 1596, this form of the word was used in English, as algorithm, by Thomas Hood.

Read

• Introduction to AlgorithmsThomas H. Cormen · 1990Book
• Algorithms to Live ByBrian Christian · 2016Book
• Data MiningMehmed Kantardzic · 2002Book
• Applied CryptographyBruce Schneier · 1994Book

Watch

• L-1.2: What is Algorithm | How to Analyze an Algorithm | Priori vs Posteriori Analysis | DAAGate SmashersVideo
• PSC algorithm |Short Sketch|Akhil NrdVideo
• The Algorithm Just Told Us What It Really IsAethersceneVideo

Listen

• AlgorithmiHeartPodcasts and TenderfootTVPodcast
• The Algorithmic AdvantageThe Algorithmic AdvantagePodcast
• Algorithms PodcastAlgorithms PodcastPodcast
• ADSP: Algorithms + Data Structures = ProgramsConor Hoekstra, Bryce Adelstein Lelbach & Ben DeanePodcast

Voices to follow

• Kate Crawford@katecrawford · XAustralian researcher
• Rasmus Pagh@RasmusPagh1 · XDanish computer scientist
• Robert Pless@rbpless · XComputer scientist
• Katharina A. Zweig@nettwerkerin · XGerman computer scientist

Debates

• Should algorithm design prioritize efficiency or readability?One view: Highly efficient algorithms are critical for handling massive datasets and real-time applications. · Another: Readable algorithms are easier to understand, debug, and maintain, reducing long-term costs and errors.Open question
• Can algorithms be truly free of bias?One view: With careful design and objective data, algorithms can be developed to treat all inputs fairly. · Another: Algorithms are created by humans and trained on human-generated data, making them susceptible to reflecting existing societal biases.Open question

Glossary

• AlgorithmA step-by-step procedure or set of rules used to solve a problem or perform a computation.
• Data StructureA particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently.
• ComplexityA measure of the resources (like time or memory) an algorithm requires as the input size grows.
• PseudocodeAn informal high-level description of an algorithm's logic, using a mix of natural language and programming constructs.
• OptimizationThe process of modifying an algorithm to make it more efficient or effective, often by reducing resource usage.
• RecursionA method where a function solves a problem by calling itself with smaller instances of the same problem until a base case is reached.

Careers

Roles this can lead toward

Student research

Published policy papers by One Young India delegates — every delegate leaves published under their own name.

• The Algorithmic Advisor: The Case For Bringing Investment Financial Services to Everyone By Means Of Robo-AdvisorsNalin Aggarwal

Threads 8

Where this connects to other fields — and why it's worth knowing.

• Logic and Valid Reasoning Philosophy

  'This sentence is false.' If it's true, it's false; if it's false, it's true. It looks like a silly word trick, but that same loop of a thing talking about itself is the exact trap that proves some problems no computer can ever solve, no matter how powerful. A brain-teaser and a hard limit on all computers turn out to be the same idea.

• Linguistics Literature

  Linguist Noam Chomsky sorted the world's grammars into a ladder, from simple to complex. Mathematicians later realized it was the very same ladder that ranks what different machines can compute. So the rules behind human sentences and the rules behind computer power turned out to be one shared theory, discovered from two totally different directions.

• Supply, Demand & Price Discovery Economics

  Figuring out how much everything in an economy should cost is a calculation too gigantic for any single computer or committee. Prices quietly solve it instead: each buyer and seller adjusts, and the whole crowd computes the answer together. That old fight over whether a government could plan an economy was really a fight about a problem too huge to compute.

• Epistemology Philosophy

  There's a proof that no computer program can ever reliably tell whether another program will run forever or eventually stop, called the halting problem. It means some things are true but no step-by-step method can ever confirm them. And since any careful 'method' of knowing is basically a procedure, the limits on computers are also limits on what anyone can ever know.

• The Scientific Method Science

  Occam's razor says the simplest explanation is usually best. Computer science makes that exact: the best theory is the shortest computer program that can spit out your data. So understanding something is really just compressing it, and 'overfitting,' a theory bloated with extra bits, is the sign you memorized instead of truly explaining.

• Procedure & Evidence Law

  Finding the culprit in a crime is brutally hard, but checking whether a given confession fits the evidence is easy. Justice quietly leans on that gap: verifying an answer is far simpler than discovering it. Computer science calls this the P-versus-NP asymmetry, the same reason we can catch fraud that we could never have predicted in advance.

• Economic Systems Economics

  Running a whole economy from the top means solving a giant math problem: who gets what, and how much. A Soviet mathematician won a Nobel treating it exactly that way. It turns out the problem is monstrously hard to compute, while markets dodge it by letting millions of people calculate at once.

• Education and Social Mobility Sociology

  Which school a kid gets into is often decided by a math recipe called the Gale-Shapley algorithm, the same one used to match people up fairly. So a big part of who climbs ahead in life is quietly handed over to a matching program.