Analgorithm is fundamentally a set of rules or defined procedures that is typically designed and used to solve a specific problem or a broad set of problems.
Broadly, algorithms define process(es), sets of rules, or methodologies that are to be followed in calculations, data processing, data mining, pattern recognition, automated reasoning or other problem-solving operations. With the increasing automation of services, more and more decisions are being made by algorithms. Some general examples are risk assessments, anticipatory policing, and pattern recognition technology.[1]
Jump search (or block search): linear search on a smaller subset of the sequence
Predictive search: binary-like search which factors inmagnitude of search term versus the high and low values in the search. Sometimes called dictionary search or interpolated search.
Ternary search: a technique for finding the minimum or maximum of a function that is either strictly increasing and then strictly decreasing or vice versa
Quicksort: divide list into two, with all items on the first list coming before all items on the second list.; then sort the two lists. Often the method of choice
Humorous or ineffective
Bogosort: the list is randomly shuffled until it happens to be sorted
Krylov methods (for large sparse matrix problems; third most-important numerical method class of the 20th century as ranked by SISC; after fast-fourier and fast-multipole)
Greedy randomized adaptive search procedure (GRASP): successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search
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 ak-mer in a sequence or sequences.
Fuzzy clustering: a class of clustering algorithms where each point has a degree of belonging to clusters
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
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
Discrete Green's theorem: is an algorithm for computing double integral over a generalized rectangular domain in constant time. It is a natural extension to the summed area table algorithm
Flood fill: fills a connected region of a multi-dimensional array with a specified symbol
Global illumination algorithms: Considers direct illumination and reflection from other objects.
Q-learning: learns an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter
Supervised learning: Learning by examples (labelled data-set split into training-set and test-set)
Support Vector Machine (SVM): a set of methods which divide multidimensional data by finding a dividing hyperplane with the maximum margin between the two sets
Structured SVM: allows training of a classifier for general structured output labels.
SIFT (Scale-invariant feature transform): is an algorithm to detect and describe local features in images.
SURF (Speeded Up Robust Features): is a robust local feature detector, first presented byHerbert Bay et al. in 2006, that can be used in computer vision tasks like object recognition or 3D reconstruction. It is partly inspired by the SIFT descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT.[9][10]
Hash function: convert a large, possibly variable-sized amount of data into a small datum, usually a single integer that may serve as an index into an array
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