Editing List of algorithms (section)

==Computational science==
{{further|Computational science}}

===Astronomy===
* [[Doomsday algorithm]]: day of the week
* [[Zeller's congruence]] is an algorithm to calculate the day of the week for any Julian or Gregorian calendar date
* various [[Computus|Easter algorithms]] are used to calculate the day of Easter

===Bioinformatics===
{{further|Bioinformatics}}
{{see also|List of algorithms#Sequence alignment|l1=Sequence alignment algorithms}}
* [[Basic Local Alignment Search Tool]] also known as BLAST: an algorithm for comparing primary biological sequence information
* [[Kabsch algorithm]]: calculate the optimal alignment of two sets of points in order to compute the [[RMSD|root mean squared deviation]] between two protein structures.
* [[Velvet (algorithm)|Velvet]]: a set of algorithms manipulating [[de Bruijn graph]]s for genomic [[sequence assembly]]
* [[Sorting by signed reversals]]: an algorithm for understanding genomic evolution.
* [[Maximum parsimony (phylogenetics)]]: an algorithm for finding the simplest phylogenetic tree to explain a given character matrix.
* [[UPGMA]]: a distance-based phylogenetic tree construction algorithm.
* [[Bloom filter|Bloom Filter]]: probabilistic data structure used to test for the existence of an element within a set. Primarily used in bioinformatics to test for the existence of a [[k-mer]] in a sequence or sequences.

===Geoscience===
{{further|Geoscience}}
* [[Vincenty's formulae]]: a fast algorithm to calculate the distance between two latitude/longitude points on an ellipsoid
* [[Geohash]]: a public domain algorithm that encodes a decimal latitude/longitude pair as a hash string

===Linguistics===
{{further|Computational linguistics|Natural language processing}}
* [[Lesk algorithm]]: word sense disambiguation
* [[Stemming|Stemming algorithm]]: a method of reducing words to their stem, base, or root form
* [[Sukhotin's algorithm]]: a statistical classification algorithm for classifying characters in a text as vowels or consonants

===Medicine===
{{further|Medical algorithms}}
* [[ESC algorithm]] for the diagnosis of heart failure
* [[Manning Criteria]] for irritable bowel syndrome
* [[Pulmonary embolism#Algorithms|Pulmonary embolism]] diagnostic algorithms
* [[Texas Medication Algorithm Project]]

===Physics===
{{further|Computational physics}}
* [[Constraint algorithm]]: a class of algorithms for satisfying constraints for bodies that obey Newton's equations of motion
* [[Demon algorithm]]: a [[Monte Carlo method]] for efficiently sampling members of a [[microcanonical ensemble]] with a given energy
* [[Featherstone's algorithm]]: computes the effects of forces applied to a structure of joints and links
* [[Ground state]] approximation
** [[Variational method]]
*** [[Ritz method]]
* [[N-body problem|''n''-body problems]]
** [[Barnes–Hut simulation]]: Solves the n-body problem in an approximate way that has the order {{math|O(<var>n</var> log <var>n</var>)}} instead of {{math|O(<var>n</var><sup>2</sup>)}} as in a direct-sum simulation.
** [[Fast multipole method]] (FMM): speeds up the calculation of long-ranged forces
* [[Rainflow-counting algorithm]]: Reduces a complex [[stress (physics)|stress]] history to a count of elementary stress-reversals for use in [[fatigue (material)|fatigue]] analysis
* [[Sweep and prune]]: a broad phase algorithm used during [[collision detection]] to limit the number of pairs of solids that need to be checked for collision
* [[VEGAS algorithm]]: a method for reducing error in [[Monte Carlo simulation]]s
* [[Glauber dynamics]]: a method for simulating the Ising Model on a computer

===Statistics===
{{further|Computational statistics}}
* [[Algorithms for calculating variance]]: avoiding instability and numerical overflow
* [[Approximate counting algorithm]]: allows counting large number of events in a small register
* [[Bayesian statistics]]
** [[Nested sampling algorithm]]: a computational approach to the problem of comparing models in Bayesian statistics
* [[Data clustering|Clustering algorithms]]
** [[UPGMA|Average-linkage clustering]]: a simple agglomerative clustering algorithm
** [[Canopy clustering algorithm]]: an unsupervised pre-clustering algorithm related to the K-means algorithm
** [[Chinese whispers (clustering method)|Chinese whispers]]
** [[Complete-linkage clustering]]: a simple agglomerative clustering algorithm
** [[DBSCAN]]: a density based clustering algorithm
** [[Expectation-maximization algorithm]]
** [[Fuzzy clustering]]: a class of clustering algorithms where each point has a degree of belonging to clusters
*** [[Fuzzy clustering#Fuzzy c-means clustering|Fuzzy c-means]]
*** [[FLAME clustering]] (Fuzzy clustering by Local Approximation of MEmberships): define clusters in the dense parts of a dataset and perform cluster assignment solely based on the neighborhood relationships among objects
** [[KHOPCA clustering algorithm]]: a local clustering algorithm, which produces hierarchical multi-hop clusters in static and mobile environments.
** [[k-means clustering]]: cluster objects based on attributes into partitions
** [[k-means++]]: a variation of this, using modified random seeds
** [[k-medoids]]: similar to k-means, but chooses datapoints or [[medoid]]s as centers
** [[Linde–Buzo–Gray algorithm]]: a vector quantization algorithm to derive a good codebook
** [[Lloyd's algorithm]] (Voronoi iteration or relaxation): group data points into a given number of categories, a popular algorithm for [[k-means clustering]]
** [[OPTICS algorithm|OPTICS]]: a density based clustering algorithm with a visual evaluation method
** [[Single-linkage clustering]]: a simple agglomerative clustering algorithm
** [[SUBCLU]]: a subspace clustering algorithm
** [[Ward's method]]: an agglomerative clustering algorithm, extended to more general Lance–Williams algorithms
** [[WACA clustering algorithm]]: a local clustering algorithm with potentially multi-hop structures; for dynamic networks
* [[Estimation theory|Estimation Theory]]
** [[Expectation-maximization algorithm]] A class of related algorithms for finding maximum likelihood estimates of parameters in probabilistic models
*** [[Ordered subset expectation maximization]] (OSEM): used in [[medical imaging]] for [[positron emission tomography]], [[single-photon emission computed tomography]] and [[X-ray]] computed tomography.
** [[Odds algorithm]] (Bruss algorithm) Optimal online search for distinguished value in sequential random input
** [[Kalman filter]]: estimate the state of a linear [[Dynamical system|dynamic system]] from a series of noisy measurements
* [[False nearest neighbor algorithm]] (FNN) estimates [[fractal dimension]]
* [[Hidden Markov model]]
** [[Baum–Welch algorithm]]: computes maximum likelihood estimates and [[Maximum a posteriori|posterior mode]] estimates for the parameters of a hidden Markov model
** [[Forward-backward algorithm]]: a dynamic programming algorithm for computing the probability of a particular observation sequence
** [[Viterbi algorithm]]: find the most likely sequence of hidden states in a hidden Markov model
* [[Partial least squares regression]]: finds a linear model describing some predicted variables in terms of other observable variables
* [[Queuing theory]]
** [[Buzen's algorithm]]: an algorithm for calculating the normalization constant G(K) in the [[Gordon–Newell theorem]]
* [[RANSAC]] (an abbreviation for "RANdom SAmple Consensus"): an iterative method to estimate parameters of a mathematical model from a set of observed data which contains outliers
* [[Scoring algorithm]]: is a form of [[Newton's method]] used to solve [[maximum likelihood]] equations numerically
* [[Yamartino method]]: calculate an approximation to the standard deviation σθ of wind direction θ during a single pass through the incoming data
* [[Ziggurat algorithm]]: generates random numbers from a non-uniform distribution