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The following is a list of algorithms along with one-line descriptions for each.

Learn Computer Science: Algorithms, Theory, and Machines from Princeton University. This course introduces the broader discipline of computer science to people having basic familiarity with Java programming. It covers the second half of our. Students who want GATE Study Material for Computer Science Engineering of Class Notes Computer can download notes from below table. Following study material is useful for GATE/IES/PSUs exam. Before downloading the notes you can check the syllabus of GATE Computer. To download GATE Computer Science Syllabus use following Link.

• 2Combinatorial algorithms
  • 2.2Graph algorithms
  • 2.3Sequence algorithms
• 3Computational mathematics
  • 3.5Numerical algorithms
• 4Computational science
• 5Computer science
  • 5.6Programming language theory
• 6Information theory and signal processing
  • 6.1Coding theory
  • 6.2Digital signal processing
• 9Distributed systems algorithms
• 11Operating systems algorithms
  • 11.3I/O scheduling

Computer Science Algorithms Pdf Template

Automated planning[edit]

Combinatorial algorithms[edit]

General combinatorial algorithms[edit]

• Brent's algorithm: finds a cycle in function value iterations using only two iterators
• Floyd's cycle-finding algorithm: finds a cycle in function value iterations
• Gale–Shapley algorithm: solves the stable marriage problem
• Pseudorandom number generators (uniformly distributed—see also List of pseudorandom number generators for other PRNGs with varying degrees of convergence and varying statistical quality):

Graph algorithms[edit]

• Coloring algorithm: Graph coloring algorithm.
• Hopcroft–Karp algorithm: convert a bipartite graph to a maximum cardinality matching
• Hungarian algorithm: algorithm for finding a perfect matching
• Prüfer coding: conversion between a labeled tree and its Prüfer sequence
• Tarjan's off-line lowest common ancestors algorithm: compute lowest common ancestors for pairs of nodes in a tree
• Topological sort: finds linear order of nodes (e.g. jobs) based on their dependencies.

Graph drawing[edit]

• Force-based algorithms (also known as force-directed algorithms or spring-based algorithm)

Network theory[edit]

• Network analysis
  • Link analysis
    • Girvan–Newman algorithm: detect communities in complex systems
    • Web link analysis
      • Hyperlink-Induced Topic Search (HITS) (also known as Hubs and authorities)
• Flow networks
  • Dinic's algorithm: is a strongly polynomial algorithm for computing the maximum flow in a flow network.
  • Edmonds–Karp algorithm: implementation of Ford–Fulkerson
  • Ford–Fulkerson algorithm: computes the maximum flow in a graph
  • Karger's algorithm: a Monte Carlo method to compute the minimum cut of a connected graph
  • Push–relabel algorithm: computes a maximum flow in a graph

Routing for graphs[edit]

• Edmonds' algorithm (also known as Chu–Liu/Edmonds' algorithm): find maximum or minimum branchings
• Euclidean minimum spanning tree: algorithms for computing the minimum spanning tree of a set of points in the plane
• Euclidean shortest path problem: find the shortest path between two points that does not intersect any obstacle
• Longest path problem: find a simple path of maximum length in a given graph
• Minimum spanning tree
• Nonblocking minimal spanning switch say, for a telephone exchange
• Shortest path problem
  • Bellman–Ford algorithm: computes shortest paths in a weighted graph (where some of the edge weights may be negative)
  • Dijkstra's algorithm: computes shortest paths in a graph with non-negative edge weights
  • Floyd–Warshall algorithm: solves the all pairs shortest path problem in a weighted, directed graph
  • Johnson's algorithm: All pairs shortest path algorithm in sparse weighted directed graph
• Transitive closure problem: find the transitive closure of a given binary relation
• Traveling salesman problem
• Warnsdorff's rule: A heuristic method for solving the Knight's tour problem.

Graph search[edit]

• A*: special case of best-first search that uses heuristics to improve speed
• B*: a best-first graph search algorithm that finds the least-cost path from a given initial node to any goal node (out of one or more possible goals)
• Backtracking: abandons partial solutions when they are found not to satisfy a complete solution
• Beam search: is a heuristic search algorithm that is an optimization of best-first search that reduces its memory requirement
• Beam stack search: integrates backtracking with beam search
• Best-first search: traverses a graph in the order of likely importance using a priority queue
• Bidirectional search: find the shortest path from an initial vertex to a goal vertex in a directed graph
• Breadth-first search: traverses a graph level by level
• Brute-force search: An exhaustive and reliable search method, but computationally inefficient in many applications.
• D*: an incremental heuristic search algorithm
• Depth-first search: traverses a graph branch by branch
• Dijkstra's algorithm: A special case of A* for which no heuristic function is used
• General Problem Solver: a seminal theorem-proving algorithm intended to work as a universal problem solver machine.
• Iterative deepening depth-first search (IDDFS): a state space search strategy
• Jump point search: An optimization to A* which may reduce computation time by an order of magnitude using further heuristics.
• Lexicographic breadth-first search (also known as Lex-BFS): a linear time algorithm for ordering the vertices of a graph
• Uniform-cost search: a tree search that finds the lowest-cost route where costs vary
• SSS*: state space search traversing a game tree in a best-first fashion similar to that of the A* search algorithm
• F*: Special algorithm to merge the two arrays

Subgraphs[edit]

• Cliques
  • Bron–Kerbosch algorithm: a technique for finding maximal cliques in an undirected graph
  • MaxCliqueDyn maximum clique algorithm: find a maximum clique in an undirected graph
• Strongly connected components

Sequence algorithms[edit]

Approximate sequence matching[edit]

• Bitap algorithm: fuzzy algorithm that determines if strings are approximately equal.
• Phonetic algorithms
  • Daitch–Mokotoff Soundex: a Soundex refinement which allows matching of Slavic and Germanic surnames
  • Double Metaphone: an improvement on Metaphone
  • Match rating approach: a phonetic algorithm developed by Western Airlines
  • Metaphone: an algorithm for indexing words by their sound, when pronounced in English
  • NYSIIS: phonetic algorithm, improves on Soundex
  • Soundex: a phonetic algorithm for indexing names by sound, as pronounced in English
• String metrics: compute a similarity or dissimilarity (distance) score between two pairs of text strings
  • Damerau–Levenshtein distance compute a distance measure between two strings, improves on Levenshtein distance
  • Dice's coefficient (also known as the Dice coefficient): a similarity measure related to the Jaccard index
  • Hamming distance: sum number of positions which are different
  • Jaro–Winkler distance: is a measure of similarity between two strings
  • Levenshtein edit distance: compute a metric for the amount of difference between two sequences
• Trigram search: search for text when the exact syntax or spelling of the target object is not precisely known

Selection algorithms[edit]

Sequence search[edit]

• Linear search: finds an item in an unsorted sequence
• Selection algorithm: finds the kth largest item in a sequence
• 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
• Sorted lists
  • Binary search algorithm: locates an item in a sorted sequence
  • Fibonacci search technique: search a sorted sequence using a divide and conquer algorithm that narrows down possible locations with the aid of Fibonacci numbers
  • Jump search (or block search): linear search on a smaller subset of the sequence
  • Predictive search: binary-like search which factors in magnitude of search term versus the high and low values in the search. Sometimes called dictionary search or interpolated search.
  • Uniform binary search: an optimization of the classic binary search algorithm

Sequence merging[edit]

Algorithms To Live By Pdf

• Simple merge algorithm
• k-way merge algorithm
• Union (merge, with elements on the output not repeated)

Sequence permutations[edit]

• Fisher–Yates shuffle (also known as the Knuth shuffle): randomly shuffle a finite set
• Schensted algorithm: constructs a pair of Young tableaux from a permutation
• Steinhaus–Johnson–Trotter algorithm (also known as the Johnson–Trotter algorithm): generate permutations by transposing elements
• Heap's permutation generation algorithm: interchange elements to generate next permutation

Sequence alignment[edit]

• Dynamic time warping: measure similarity between two sequences which may vary in time or speed
• Hirschberg's algorithm: finds the least cost sequence alignment between two sequences, as measured by their Levenshtein distance
• Needleman–Wunsch algorithm: find global alignment between two sequences
• Smith–Waterman algorithm: find local sequence alignment

Sequence sorting[edit]

• Exchange sorts
  • Bubble sort: for each pair of indices, swap the items if out of order
  • Cocktail shaker sort or bidirectional bubble sort, a bubble sort traversing the list alternately from front to back and back to front
  • 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
• Hybrid
  • Introsort: begin with quicksort and switch to heapsort when the recursion depth exceeds a certain level
  • Timsort: adaptative algorithm derived from merge sort and insertion sort. Used in Python 2.3 and up, and Java SE 7.
• Insertion sorts
  • Insertion sort: determine where the current item belongs in the list of sorted ones, and insert it there
  • Shell sort: an attempt to improve insertion sort
  • Tree sort (binary tree sort): build binary tree, then traverse it to create sorted list
  • Cycle sort: in-place with theoretically optimal number of writes
• Merge sorts
  • Merge sort: sort the first and second half of the list separately, then merge the sorted lists
• Non-comparison sorts
  • Burstsort: build a compact, cache efficient burst trie and then traverse it to create sorted output
  • Postman sort: variant of Bucket sort which takes advantage of hierarchical structure
  • Radix sort: sorts strings letter by letter
• Selection sorts
  • Heapsort: convert the list into a heap, keep removing the largest element from the heap and adding it to the end of the list
  • Selection sort: pick the smallest of the remaining elements, add it to the end of the sorted list
• Other
• Unknown class

Subsequences[edit]

• Kadane's algorithm: finds maximum sub-array of any size
• Longest common subsequence problem: Find the longest subsequence common to all sequences in a set of sequences
• Longest increasing subsequence problem: Find the longest increasing subsequence of a given sequence
• Shortest common supersequence problem: Find the shortest supersequence that contains two or more sequences as subsequences

Substrings[edit]

• Longest common substring problem: find the longest string (or strings) that is a substring (or are substrings) of two or more strings
• Substring search
  • Aho–Corasick string matching algorithm: trie based algorithm for finding all substring matches to any of a finite set of strings
  • Boyer–Moore string-search algorithm: amortized linear (sublinear in most times) algorithm for substring search
  • Boyer–Moore–Horspool algorithm: Simplification of Boyer–Moore
  • Knuth–Morris–Pratt algorithm: substring search which bypasses reexamination of matched characters
  • Rabin–Karp string search algorithm: searches multiple patterns efficiently
  • Zhu–Takaoka string matching algorithm: a variant of Boyer–Moore
• Ukkonen's algorithm: a linear-time, online algorithm for constructing suffix trees
• Matching wildcards
  • Rich Salz' wildmat: a widely used open-sourcerecursive algorithm
  • Krauss matching wildcards algorithm: an open-source non-recursive algorithm

Computational mathematics[edit]

Abstract algebra[edit]

• Chien search: a recursive algorithm for determining roots of polynomials defined over a finite field
• Schreier–Sims algorithm: computing a base and strong generating set (BSGS) of a permutation group
• Todd–Coxeter algorithm: Procedure for generating cosets.

Computer algebra[edit]

• Buchberger's algorithm: finds a Gröbner basis
• Cantor–Zassenhaus algorithm: factor polynomials over finite fields
• Faugère F4 algorithm: finds a Gröbner basis (also mentions the F5 algorithm)
• Gosper's algorithm: find sums of hypergeometric terms that are themselves hypergeometric terms
• Knuth–Bendix completion algorithm: for rewriting rule systems
• Multivariate division algorithm: for polynomials in several indeterminates
• Pollard's kangaroo algorithm (also known as Pollard's lambda algorithm ): an algorithm for solving the discrete logarithm problem
• Polynomial long division: an algorithm for dividing a polynomial by another polynomial of the same or lower degree
• Risch algorithm: an algorithm for the calculus operation of indefinite integration (i.e. finding antiderivatives)

Geometry[edit]

• Closest pair problem: find the pair of points (from a set of points) with the smallest distance between them
• Collision detection algorithms: check for the collision or intersection of two given solids
• Cone algorithm: identify surface points
• Convex hull algorithms: determining the convex hull of a set of points
  • Gift wrapping algorithm or Jarvis march
• Euclidean distance transform: computes the distance between every point in a grid and a discrete collection of points.
• Geometric hashing: a method for efficiently finding two-dimensional objects represented by discrete points that have undergone an affine transformation
• Gilbert–Johnson–Keerthi distance algorithm: determining the smallest distance between two convex shapes.
• Jump-and-Walk algorithm: an algorithm for point location in triangulations
• Laplacian smoothing: an algorithm to smooth a polygonal mesh
• Line segment intersection: finding whether lines intersect, usually with a sweep line algorithm
• Minimum bounding box algorithms: find the oriented minimum bounding box enclosing a set of points
• Nearest neighbor search: find the nearest point or points to a query point
• Point in polygon algorithms: tests whether a given point lies within a given polygon
• Point set registration algorithms: finds the transformation between two point sets to optimally align them.
• Rotating calipers: determine all antipodal pairs of points and vertices on a convex polygon or convex hull.
• Shoelace algorithm: determine the area of a polygon whose vertices are described by ordered pairs in the plane
• Triangulation
  • Delaunay triangulation
    • Ruppert's algorithm (also known as Delaunay refinement): create quality Delaunay triangulations
    • Chew's second algorithm: create quality constrained Delaunay triangulations
  • Marching triangles: reconstruct two-dimensional surface geometry from an unstructured point cloud
  • Polygon triangulation algorithms: decompose a polygon into a set of triangles
  • Voronoi diagrams, geometric dual of Delaunay triangulation
    • Bowyer–Watson algorithm: create voronoi diagram in any number of dimensions
    • Fortune's Algorithm: create voronoi diagram

Number theoretic algorithms[edit]

• Binary GCD algorithm: Efficient way of calculating GCD.
• Chakravala method: a cyclic algorithm to solve indeterminate quadratic equations, including Pell's equation
• Discrete logarithm:
• Euclidean algorithm: computes the greatest common divisor
• Extended Euclidean algorithm: Also solves the equation ax + by = c.
• Integer factorization: breaking an integer into its prime factors
• Multiplication algorithms: fast multiplication of two numbers
• Modular square root: computing square roots modulo a prime number
• Odlyzko–Schönhage algorithm: calculates nontrivial zeroes of the Riemann zeta function
• Lenstra–Lenstra–Lovász algorithm (also known as LLL algorithm): find a short, nearly orthogonal latticebasis in polynomial time
• Primality tests: determining whether a given number is prime

Numerical algorithms[edit]

Differential equation solving[edit]

• Runge–Kutta methods
• Multigrid methods (MG methods), a group of algorithms for solving differential equations using a hierarchy of discretizations
• Partial differential equation:
  • Crank–Nicolson method for diffusion equations
  • Lax-Wendroff for wave equations
• Verlet integration (French pronunciation: ​[vɛʁˈlɛ]): integrate Newton's equations of motion

Elementary and special functions[edit]

• Computation of π:
  • Borwein's algorithm: an algorithm to calculate the value of 1/π
  • Gauss–Legendre algorithm: computes the digits of pi
  • Chudnovsky algorithm: A fast method for calculating the digits of π
  • Bailey–Borwein–Plouffe formula: (BBP formula) a spigot algorithm for the computation of the nth binary digit of π
• Division algorithms: for computing quotient and/or remainder of two numbers
  • Newton–Raphson division: uses Newton's method to find the reciprocal of D, and multiply that reciprocal by N to find the final quotient Q.
• Hyperbolic and Trigonometric Functions:
  • BKM algorithm: compute elementary functions using a table of logarithms
  • CORDIC: compute hyperbolic and trigonometric functions using a table of arctangents
• Exponentiation:
  • Addition-chain exponentiation: exponentiation by positive integer powers that requires a minimal number of multiplications
  • Exponentiating by squaring: an algorithm used for the fast computation of large integer powers of a number
• Montgomery reduction: an algorithm that allows modular arithmetic to be performed efficiently when the modulus is large
• Multiplication algorithms: fast multiplication of two numbers
  • Booth's multiplication algorithm: a multiplication algorithm that multiplies two signed binary numbers in two's complement notation
  • Fürer's algorithm: an integer multiplication algorithm for very large numbers possessing a very low asymptotic complexity
  • Karatsuba algorithm: an efficient procedure for multiplying large numbers
  • Schönhage–Strassen algorithm: an asymptotically fast multiplication algorithm for large integers
  • Toom–Cook multiplication: (Toom3) a multiplication algorithm for large integers
• Multiplicative inverse Algorithms: for computing a number's multiplicative inverse (reciprocal).
• Rounding functions: the classic ways to round numbers
• Spigot algorithm: A way to compute the value of a mathematical constant without knowing preceding digits
• Square and Nth root of a number:
  • Alpha max plus beta min algorithm: an approximation of the square-root of the sum of two squares
  • Shifting nth-root algorithm: digit by digit root extraction
• Summation:
  • Binary splitting: a divide and conquer technique which speeds up the numerical evaluation of many types of series with rational terms
  • Kahan summation algorithm: a more accurate method of summing floating-point numbers

Geometric[edit]

• Filtered back-projection: efficiently compute the inverse 2-dimensional Radon transform.
• Level set method (LSM): a numerical technique for tracking interfaces and shapes

Interpolation and extrapolation[edit]

• Birkhoff interpolation: an extension of polynomial interpolation
• Lagrange interpolation: interpolation using Lagrange polynomials
• Linear interpolation: a method of curve fitting using linear polynomials
• Monotone cubic interpolation: a variant of cubic interpolation that preserves monotonicity of the data set being interpolated.
• Multivariate interpolation
  • Bicubic interpolation, a generalization of cubic interpolation to two dimensions
  • Bilinear interpolation: an extension of linear interpolation for interpolating functions of two variables on a regular grid
  • Lanczos resampling ('Lanzosh'): a multivariate interpolation method used to compute new values for any digitally sampled data
  • Tricubic interpolation, a generalization of cubic interpolation to three dimensions
• Pareto interpolation: a method of estimating the median and other properties of a population that follows a Pareto distribution.
• Polynomial interpolation
• Spline interpolation: Reduces error with Runge's phenomenon.
  • De Boor algorithm: B-splines
  • De Casteljau's algorithm: Bézier curves

Linear algebra[edit]

• Eigenvalue algorithms
• Gram–Schmidt process: orthogonalizes a set of vectors
• Matrix multiplication algorithms
  • Cannon's algorithm: a distributed algorithm for matrix multiplication especially suitable for computers laid out in an N × N mesh
  • Coppersmith–Winograd algorithm: square matrix multiplication
  • Freivalds' algorithm: a randomized algorithm used to verify matrix multiplication
  • Strassen algorithm: faster matrix multiplication

• Solving systems of linear equations
  • Biconjugate gradient method: solves systems of linear equations
  • Conjugate gradient: an algorithm for the numerical solution of particular systems of linear equations
  • Gauss–Jordan elimination: solves systems of linear equations
  • Gauss–Seidel method: solves systems of linear equations iteratively
  • Levinson recursion: solves equation involving a Toeplitz matrix
  • Stone's method: also known as the strongly implicit procedure or SIP, is an algorithm for solving a sparse linear system of equations
  • Successive over-relaxation (SOR): method used to speed up convergence of the Gauss–Seidel method
  • Tridiagonal matrix algorithm (Thomas algorithm): solves systems of tridiagonal equations
• Sparse matrix algorithms
  • Cuthill–McKee algorithm: reduce the bandwidth of a symmetric sparse matrix
  • Minimum degree algorithm: permute the rows and columns of a symmetric sparse matrix before applying the Cholesky decomposition
  • Symbolic Cholesky decomposition: Efficient way of storing sparse matrix

Monte Carlo[edit]

• Gibbs sampling: generate a sequence of samples from the joint probability distribution of two or more random variables
• Hybrid Monte Carlo: generate a sequence of samples using Hamiltonian weighted Markov chain Monte Carlo, from a probability distribution which is difficult to sample directly.
• Metropolis–Hastings algorithm: used to generate a sequence of samples from the probability distribution of one or more variables
• Wang and Landau algorithm: an extension of Metropolis–Hastings algorithm sampling

Numerical integration[edit]

• MISER algorithm: Monte Carlo simulation, numerical integration

Root finding[edit]

• False position method: approximates roots of a function
• Newton's method: finds zeros of functions with calculus
• Halley's method: uses first and second derivatives
• Secant method: 2-point, 1-sided
• False position method and Illinois method: 2-point, bracketing
• Ridder's method: 3-point, exponential scaling
• Muller's method: 3-point, quadratic interpolation

Optimization algorithms[edit]

• Alpha–beta pruning: search to reduce number of nodes in minimax algorithm
• Bruss algorithm: see odds algorithm
• Combinatorial optimization: optimization problems where the set of feasible solutions is discrete
  • Greedy randomized adaptive search procedure (GRASP): successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search
  • Hungarian method: a combinatorial optimization algorithm which solves the assignment problem in polynomial time
• Constraint satisfaction
  • General algorithms for the constraint satisfaction
  • Chaff algorithm: an algorithm for solving instances of the boolean satisfiability problem
  • Davis–Putnam algorithm: check the validity of a first-order logic formula
  • Davis–Putnam–Logemann–Loveland algorithm (DPLL): an algorithm for deciding the satisfiability of propositional logic formula in conjunctive normal form, i.e. for solving the CNF-SAT problem
  • Exact cover problem
    • Algorithm X: a nondeterministic algorithm
    • Dancing Links: an efficient implementation of Algorithm X
• Cross-entropy method: a general Monte Carlo approach to combinatorial and continuous multi-extremal optimization and importance sampling
• Dynamic Programming: problems exhibiting the properties of overlapping subproblems and optimal substructure
• Ellipsoid method: is an algorithm for solving convex optimization problems
• Evolutionary computation: optimization inspired by biological mechanisms of evolution
  • Genetic algorithms
    • Fitness proportionate selection - also known as roulette-wheel selection
  • Swarm intelligence
    • Bees algorithm: a search algorithm which mimics the food foraging behavior of swarms of honey bees
• golden section search: an algorithm for finding the maximum of a real function
• Harmony search (HS): a metaheuristic algorithm mimicking the improvisation process of musicians
• Linear programming
  • Benson's algorithm: an algorithm for solving linear vector optimization problems
  • Dantzig–Wolfe decomposition: an algorithm for solving linear programming problems with special structure
  • Integer linear programming: solve linear programming problems where some or all the unknowns are restricted to integer values
  • Karmarkar's algorithm: The first reasonably efficient algorithm that solves the linear programming problem in polynomial time.
  • Simplex algorithm: An algorithm for solving linear programming problems
• Local search: a metaheuristic for solving computationally hard optimization problems
• Minimax used in game programming
• Nearest neighbor search (NNS): find closest points in a metric space
  • Best Bin First: find an approximate solution to the Nearest neighbor search problem in very-high-dimensional spaces
• Nonlinear optimization
  • BFGS method: A nonlinear optimization algorithm
  • Gauss–Newton algorithm: An algorithm for solving nonlinear least squares problems.
  • Levenberg–Marquardt algorithm: An algorithm for solving nonlinear least squares problems.
  • Nelder–Mead method (downhill simplex method): A nonlinear optimization algorithm
• Odds algorithm (Bruss algorithm) : Finds the optimal strategy to predict a last specific event in a random sequence event
• Subset sum algorithm

Computational science[edit]

Astronomy[edit]

• 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 Easter algorithms are used to calculate the day of Easter

Bioinformatics[edit]

• 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 root mean squared deviation between two protein structures.
• Velvet: a set of algorithms manipulating de Bruijn graphs 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.

Geoscience[edit]

• 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[edit]

• Lesk algorithm: word sense disambiguation
• 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[edit]

• ESC algorithm for the diagnosis of heart failure
• Manning Criteria for irritable bowel syndrome
• Pulmonary embolism diagnostic algorithms

Physics[edit]

• 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: compute the effects of forces applied to a structure of joints and links
• Ground state approximation
  • Variational method
• N-body problems
  • Barnes–Hut simulation: Solves the n-body problem in an approximate way that has the order O(n log n) instead of O(n²) 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 history to a count of elementary stress-reversals for use in 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 simulations

Statistics[edit]

• 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
• Clustering Algorithms
  • Average-linkage clustering: a simple agglomerative clustering algorithm
  • Canopy clustering algorithm: an unsupervised pre-clustering algorithm related to the K-means algorithm
  • Complete-linkage clustering: a simple agglomerative clustering algorithm
  • DBSCAN: a density based clustering algorithm
  • 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
  • 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 medoids 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: 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
  • 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 dynamic system from a series of noisy measurements
• False nearest neighbor algorithm (FNN) estimates fractal dimension
• Hidden Markov model
  • Baum–Welch algorithm: compute maximum likelihood estimates and 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: generate random numbers from a non-uniform distribution

Computer science[edit]

Computer architecture[edit]

• Tomasulo algorithm: allows sequential instructions that would normally be stalled due to certain dependencies to execute non-sequentially

Computer graphics[edit]

• Clipping
  • Line clipping
  • Polygon clipping
• Contour lines and Isosurfaces
  • Marching cubes: extract a polygonal mesh of an isosurface from a three-dimensional scalar field (sometimes called voxels)
  • Marching squares: generate contour lines for a two-dimensional scalar field
  • Marching tetrahedrons: an alternative to Marching cubes
• 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.
• Hidden surface removal or Visual surface determination
  • Newell's algorithm: eliminate polygon cycles in the depth sorting required in hidden surface removal
  • Painter's algorithm: detects visible parts of a 3-dimensional scenery
  • Scanline rendering: constructs an image by moving an imaginary line over the image
• Line Drawing: graphical algorithm for approximating a line segment on discrete graphical media.
  • Bresenham's line algorithm: plots points of a 2-dimensional array to form a straight line between 2 specified points (uses decision variables)
  • DDA line algorithm: plots points of a 2-dimensional array to form a straight line between 2 specified points (uses floating-point math)
  • Xiaolin Wu's line algorithm: algorithm for line antialiasing.
• Midpoint circle algorithm: an algorithm used to determine the points needed for drawing a circle
• Ramer–Douglas–Peucker algorithm: Given a 'curve' composed of line segments to find a curve not too dissimilar but that has fewer points
• Shading
  • Gouraud shading: an algorithm to simulate the differing effects of light and colour across the surface of an object in 3D computer graphics
  • Phong shading: an algorithm to interpolate surface normal-vectors for surface shading in 3D computer graphics
• Slerp (spherical linear interpolation): quaternion interpolation for the purpose of animating 3D rotation
• Summed area table (also known as an integral image): an algorithm for computing the sum of values in a rectangular subset of a grid in constant time

Cryptography[edit]

• Asymmetric (public key) encryption:
• Digital signatures (asymmetric authentication):
  • DSA, and its variants:
    • ECDSA and Deterministic ECDSA
    • EdDSA (Ed25519)
• Cryptographic hash functions (see also the section on message authentication codes):
  • MD5 – Note that there is now a method of generating collisions for MD5
  • SHA-1 – Note that there is now a method of generating collisions for SHA-1
  • SHA-2 (SHA-224, SHA-256, SHA-384, SHA-512)
  • SHA-3 (SHA3-224, SHA3-256, SHA3-384, SHA3-512, SHAKE128, SHAKE256)
  • Tiger (TTH), usually used in Tiger tree hashes
• Cryptographically secure pseudo-random number generators
  • Blum Blum Shub - based on the hardness of factorization
  • Fortuna, intended as an improvement on Yarrow algorithm
  • Linear-feedback shift register (note: many LFSR-based algorithms are weak or have been broken)
• Key exchange
  • Elliptic-curve Diffie-Hellman (ECDH)
• Key derivation functions, often used for password hashing and key stretching
• Message authentication codes (symmetric authentication algorithms, which take a key as a parameter):
  • HMAC: keyed-hash message authentication
• Secret sharing, Secret Splitting, Key Splitting, M of N algorithms
  • Blakey's Scheme
• Symmetric (secret key) encryption:
  • Advanced Encryption Standard (AES), winner of NIST competition, also known as Rijndael
  • Data Encryption Standard (DES), sometimes DE Algorithm, winner of NBS selection competition, replaced by AES for most purposes
  • Tiny Encryption Algorithm (TEA)
  • Salsa20, and its updated variant ChaCha20

Digital logic[edit]

• Boolean minimization
  • Quine–McCluskey algorithm: Also called as Q-M algorithm, programmable method for simplifying the boolean equations.
  • Petrick's method: Another algorithm for boolean simplification.
  • Espresso heuristic logic minimizer: Fast algorithm for boolean function minimization.

Machine learning and statistical classification[edit]

• ALOPEX: a correlation-based machine-learning algorithm
• Association rule learning: discover interesting relations between variables, used in data mining
• Boosting (meta-algorithm): Use many weak learners to boost effectiveness
  • AdaBoost: adaptive boosting
  • BrownBoost:a boosting algorithm that may be robust to noisy datasets
  • LogitBoost: logistic regression boosting
  • LPBoost: linear programming boosting
• Bootstrap aggregating (bagging): technique to improve stability and classification accuracy
• Computer Vision
  • Grabcut based on Graph cuts
• Decision Trees
  • C4.5 algorithm: an extension to ID3
  • ID3 algorithm (Iterative Dichotomiser 3): Use heuristic to generate small decision trees
• Clustering: Class of unsupervised learning algorithms for grouping and bucketing related input vector.
  • k-nearest neighbors (k-NN): a method for classifying objects based on closest training examples in the feature space
• Linde–Buzo–Gray algorithm: a vector quantization algorithm used to derive a good codebook
• Locality-sensitive hashing (LSH): a method of performing probabilistic dimension reduction of high-dimensional data
• Neural Network
  • Backpropagation: A supervised learning method which requires a teacher that knows, or can calculate, the desired output for any given input
  • Hopfield net: a Recurrent neural network in which all connections are symmetric
  • Perceptron: the simplest kind of feedforward neural network: a linear classifier.
  • Pulse-coupled neural networks (PCNN): Neural models proposed by modeling a cat's visual cortex and developed for high-performance biomimetic image processing.
  • Radial basis function network: an artificial neural network that uses radial basis functions as activation functions
  • Self-organizing map: an unsupervised network that produces a low-dimensional representation of the input space of the training samples
• Random forest: classify using many decision trees
• Reinforcement Learning:
  • Q-learning: learn an action-value function that gives the expected utility of taking a given action in a given state and following a fixed policy thereafter
  • State-Action-Reward-State-Action (SARSA): learn a Markov decision process policy
• Relevance Vector Machine (RVM): similar to SVM, but provides probabilistic classification
• Supervised Learning: Learning by examples (labelled data like audio signals or photographic images
• Vector quantization: technique often used in lossy data compression

Digital signal processing[edit]

• Adaptive-additive algorithm (AA algorithm): find the spatial frequency phase of an observed wave source
• Discrete Fourier transform: determines the frequencies contained in a (segment of a) signal
• Fast folding algorithm: an efficient algorithm for the detection of approximately periodic events within time series data
• Gerchberg–Saxton algorithm: Phase retrieval algorithm for optical planes
• Goertzel algorithm: identify a particular frequency component in a signal. Can be used for DTMF digit decoding.
• Karplus-Strong string synthesis: physical modelling synthesis to simulate the sound of a hammered or plucked string or some types of percussion

Image processing[edit]

• Contrast Enhancement
  • Histogram equalization: use histogram to improve image contrast
  • Adaptive histogram equalization: histogram equalization which adapts to local changes in contrast
• Connected-component labeling: find and label disjoint regions
• Dithering and half-toning
• Elser difference-map algorithm: a search algorithm for general constraint satisfaction problems. Originally used for X-Ray diffraction microscopy
• Feature detection
  • Canny edge detector: detect a wide range of edges in images
  • Marr–Hildreth algorithm: an early edge detection algorithm
  • 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 by Herbert 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.^[2][3]
• Richardson–Lucy deconvolution: image de-blurring algorithm
• Blind deconvolution: image de-blurring algorithm when point spread function is unknown.
• Seam carving: content-aware image resizing algorithm
• Segmentation: partition a digital image into two or more regions
  • GrowCut algorithm: an interactive segmentation algorithm
  • Watershed transformation: a class of algorithms based on the watershed analogy

Software engineering[edit]

• CHS conversion: converting between disk addressing systems
• Double dabble: Convert binary numbers to BCD
• 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
  • Fowler–Noll–Vo hash function: fast with low collision rate
  • Pearson hashing: computes 8 bit value only, optimized for 8 bit computers
  • Zobrist hashing: used in the implementation of transposition tables
• Xor swap algorithm: swaps the values of two variables without using a buffer

Database algorithms[edit]

• Algorithms for Recovery and Isolation Exploiting Semantics (ARIES): transaction recovery
• Join algorithms

Distributed systems algorithms[edit]

• Bully algorithm: a method for dynamically selecting a coordinator
• Byzantine fault tolerance: good fault tolerance.
• Clock synchronization
• Detection of Process Termination
• Lamport ordering: a partial ordering of events based on the happened-before relation
• Mutual exclusion
• Paxos algorithm: a family of protocols for solving consensus in a network of unreliable processors
• Snapshot algorithm: record a consistent global state for an asynchronous system
• Vector clocks: generate a partial ordering of events in a distributed system and detect causality violations

Memory allocation and deallocation algorithms[edit]

• Buddy memory allocation: Algorithm to allocate memory such that fragmentation is less.
• Garbage collectors
  • Cheney's algorithm: An improvement on the Semi-space collector
  • Generational garbage collector: Fast garbage collectors that segregate memory by age
  • Mark-compact algorithm: a combination of the mark-sweep algorithm and Cheney's copying algorithm
  • Semi-space collector: An early copying collector

Networking[edit]

• Karn's algorithm: addresses the problem of getting accurate estimates of the round-trip time for messages when using TCP
• Luleå algorithm: a technique for storing and searching internet routing tables efficiently
• Network congestion
  • Nagle's algorithm: improve the efficiency of TCP/IP networks by coalescing packets

Operating systems algorithms[edit]

• Banker's algorithm: Algorithm used for deadlock avoidance.
• Page replacement algorithms: Selecting the victim page under low memory conditions.
  • Adaptive replacement cache: better performance than LRU
  • Clock with Adaptive Replacement (CAR): is a page replacement algorithm that has performance comparable to Adaptive replacement cache

Process synchronization[edit]

Scheduling[edit]

• Top-nodes algorithm: resource calendar management

I/O scheduling[edit]

Disk scheduling[edit]

• Elevator algorithm: Disk scheduling algorithm that works like an elevator.
• Shortest seek first: Disk scheduling algorithm to reduce seek time.

See also[edit]

References[edit]

1. ^[1]
2. ^[2]
3. ^'Archived copy'(PDF). Archived from the original(PDF) on 2013-10-06. Retrieved 2013-10-05.CS1 maint: archived copy as title (link)

Computer science (sometimes called computation science or computing science, but not to be confused with computational science or software engineering) is the study of processes that interact with data and that can be represented as data in the form of programs. It enables the use of algorithms to manipulate, store, and communicatedigital information. A computer scientist studies the theory of computation and the practice of designingsoftware systems.^[1]

Its fields can be divided into theoretical and practical disciplines. Computational complexity theory is highly abstract, while computer graphics emphasizes real-world applications. Programming language theory considers approaches to the description of computational processes, while computer programming itself involves the use of programming languages and complex systems. Human–computer interaction considers the challenges in making computers useful, usable, and accessible.

• 1History
• 4Fields
  • 4.1Theoretical computer science
  • 4.2Computer systems
  • 4.3Computer applications
• 8Education
• 12Further reading
• 13External links

History[edit]

The earliest foundations of what would become computer science predate the invention of the modern digital computer. Machines for calculating fixed numerical tasks such as the abacus have existed since antiquity, aiding in computations such as multiplication and division. Algorithms for performing computations have existed since antiquity, even before the development of sophisticated computing equipment.

Wilhelm Schickard designed and constructed the first working mechanical calculator in 1623.^[4] In 1673, Gottfried Leibniz demonstrated a digital mechanical calculator, called the Stepped Reckoner.^[5] He may be considered the first computer scientist and information theorist, for, among other reasons, documenting the binary number system. In 1820, Thomas de Colmar launched the mechanical calculator industry^{[note 1]} when he released his simplified arithmometer, which was the first calculating machine strong enough and reliable enough to be used daily in an office environment. Charles Babbage started the design of the first automatic mechanical calculator, his Difference Engine, in 1822, which eventually gave him the idea of the first programmable mechanical calculator, his Analytical Engine.^[6] He started developing this machine in 1834, and 'in less than two years, he had sketched out many of the salient features of the modern computer'.^[7] 'A crucial step was the adoption of a punched card system derived from the Jacquard loom'^[7] making it infinitely programmable.^{[note 2]} In 1843, during the translation of a French article on the Analytical Engine, Ada Lovelace wrote, in one of the many notes she included, an algorithm to compute the Bernoulli numbers, which is considered to be the first published algorithm ever specifically tailored for implementation on a computer.^[8] Around 1885, Herman Hollerith invented the tabulator, which used punched cards to process statistical information; eventually his company became part of IBM. Following Babbage, although unaware of his earlier work, Percy Ludgate in 1909 published ^[9] the 2nd of the only two designs for mechanical analytical engines in history. In 1937, one hundred years after Babbage's impossible dream, Howard Aiken convinced IBM, which was making all kinds of punched card equipment and was also in the calculator business^[10] to develop his giant programmable calculator, the ASCC/Harvard Mark I, based on Babbage's Analytical Engine, which itself used cards and a central computing unit. When the machine was finished, some hailed it as 'Babbage's dream come true'.^[11]

During the 1940s, as new and more powerful computing machines such as the Atanasoff–Berry computer and ENIAC were developed, the term computer came to refer to the machines rather than their human predecessors.^[12] As it became clear that computers could be used for more than just mathematical calculations, the field of computer science broadened to study computation in general. In 1945, IBM founded the Watson Scientific Computing Laboratory at Columbia University in New York City. The renovated fraternity house on Manhattan's West Side was IBM's first laboratory devoted to pure science. The lab is the forerunner of IBM's Research Division, which today operates research facilities around the world.^[13] Ultimately, the close relationship between IBM and the university was instrumental in the emergence of a new scientific discipline, with Columbia offering one of the first academic-credit courses in computer science in 1946.^[14] Computer science began to be established as a distinct academic discipline in the 1950s and early 1960s.^[15][16] The world's first computer science degree program, the Cambridge Diploma in Computer Science, began at the University of CambridgeComputer Laboratory in 1953. The first computer science department in the United States was formed at Purdue University in 1962.^[17] Since practical computers became available, many applications of computing have become distinct areas of study in their own rights.

Although many initially believed it was impossible that computers themselves could actually be a scientific field of study, in the late fifties it gradually became accepted among the greater academic population.^[18][19] It is the now well-known IBM brand that formed part of the computer science revolution during this time. IBM (short for International Business Machines) released the IBM 704^[20] and later the IBM 709^[21] computers, which were widely used during the exploration period of such devices. 'Still, working with the IBM [computer] was frustrating […] if you had misplaced as much as one letter in one instruction, the program would crash, and you would have to start the whole process over again'.^[18] During the late 1950s, the computer science discipline was very much in its developmental stages, and such issues were commonplace.^[19]

Time has seen significant improvements in the usability and effectiveness of computing technology.^[22] Modern society has seen a significant shift in the users of computer technology, from usage only by experts and professionals, to a near-ubiquitous user base. Initially, computers were quite costly, and some degree of humanitarian aid was needed for efficient use—in part from professional computer operators. As computer adoption became more widespread and affordable, less human assistance was needed for common usage.

Contributions[edit]

Despite its short history as a formal academic discipline, computer science has made a number of fundamental contributions to science and society—in fact, along with electronics, it is a founding science of the current epoch of human history called the Information Age and a driver of the information revolution, seen as the third major leap in human technological progress after the Industrial Revolution (1750–1850 CE) and the Agricultural Revolution (8000–5000 BCE).

These contributions include:

• The start of the 'Digital Revolution', which includes the current Information Age and the Internet.^[24]
• A formal definition of computation and computability, and proof that there are computationally unsolvable and intractable problems.^[25]
• The concept of a programming language, a tool for the precise expression of methodological information at various levels of abstraction.^[26]
• In cryptography, breaking the Enigma code was an important factor contributing to the Allied victory in World War II.^[23]
• Scientific computing enabled practical evaluation of processes and situations of great complexity, as well as experimentation entirely by software. It also enabled advanced study of the mind, and mapping of the human genome became possible with the Human Genome Project.^[24]Distributed computing projects such as Folding@home explore protein folding.
• Algorithmic trading has increased the efficiency and liquidity of financial markets by using artificial intelligence, machine learning, and other statistical and numerical techniques on a large scale.^[27] High frequency algorithmic trading can also exacerbate volatility.^[28]
• Computer graphics and computer-generated imagery have become ubiquitous in modern entertainment, particularly in television, cinema, advertising, animation and video games. Even films that feature no explicit CGI are usually 'filmed' now on digital cameras, or edited or post-processed using a digital video editor.^[29][30]
• Simulation of various processes, including computational fluid dynamics, physical, electrical, and electronic systems and circuits, as well as societies and social situations (notably war games) along with their habitats, among many others. Modern computers enable optimization of such designs as complete aircraft. Notable in electrical and electronic circuit design are SPICE,^[31] as well as software for physical realization of new (or modified) designs. The latter includes essential design software for integrated circuits.^{[citation needed]}
• Artificial intelligence is becoming increasingly important as it gets more efficient and complex. There are many applications of AI, some of which can be seen at home, such as robotic vacuum cleaners. It is also present in video games and on the modern battlefield in drones, anti-missile systems, and squad support robots.^[32]
• Human–computer interaction combines novel algorithms with design strategies that enable rapid human performance, low error rates, ease in learning, and high satisfaction. Researchers use ethnographic observation and automated data collection to understand user needs, then conduct usability tests to refine designs. Key innovations include the direct manipulation, selectable web links, touchscreen designs, mobile applications, and virtual reality.

Etymology[edit]

Although first proposed in 1956,^[19] the term 'computer science' appears in a 1959 article in Communications of the ACM,^[33]in which Louis Fein argues for the creation of a Graduate School in Computer Sciences analogous to the creation of Harvard Business School in 1921,^[34] justifying the name by arguing that, like management science, the subject is applied and interdisciplinary in nature, while having the characteristics typical of an academic discipline.^[33]His efforts, and those of others such as numerical analystGeorge Forsythe, were rewarded: universities went on to create such departments, starting with Purdue in 1962.^[35] Despite its name, a significant amount of computer science does not involve the study of computers themselves. Because of this, several alternative names have been proposed.^[36] Certain departments of major universities prefer the term computing science, to emphasize precisely that difference. Danish scientist Peter Naur suggested the term datalogy,^[37] to reflect the fact that the scientific discipline revolves around data and data treatment, while not necessarily involving computers. The first scientific institution to use the term was the Department of Datalogy at the University of Copenhagen, founded in 1969, with Peter Naur being the first professor in datalogy. The term is used mainly in the Scandinavian countries. An alternative term, also proposed by Naur, is data science; this is now used for a multi-disciplinary field of data analysis, including statistics and databases.

Also, in the early days of computing, a number of terms for the practitioners of the field of computing were suggested in the Communications of the ACM—turingineer, turologist, flow-charts-man, applied meta-mathematician, and applied epistemologist.^[38] Three months later in the same journal, comptologist was suggested, followed next year by hypologist.^[39] The term computics has also been suggested.^[40]In Europe, terms derived from contracted translations of the expression 'automatic information' (e.g. 'informazione automatica' in Italian) or 'information and mathematics' are often used, e.g. informatique (French), Informatik (German), informatica (Italian, Dutch), informática (Spanish, Portuguese), informatika (Slavic languages and Hungarian) or pliroforiki (πληροφορική, which means informatics) in Greek. Similar words have also been adopted in the UK (as in the School of Informatics of the University of Edinburgh).^[41]'In the U.S., however, informatics is linked with applied computing, or computing in the context of another domain.'^[42]

A folkloric quotation, often attributed to—but almost certainly not first formulated by—Edsger Dijkstra, states that 'computer science is no more about computers than astronomy is about telescopes.'^{[note 3]} The design and deployment of computers and computer systems is generally considered the province of disciplines other than computer science. For example, the study of computer hardware is usually considered part of computer engineering, while the study of commercial computer systems and their deployment is often called information technology or information systems. However, there has been much cross-fertilization of ideas between the various computer-related disciplines. Computer science research also often intersects other disciplines, such as philosophy, cognitive science, linguistics, mathematics, physics, biology, statistics, and logic.

Computer science is considered by some to have a much closer relationship with mathematics than many scientific disciplines, with some observers saying that computing is a mathematical science.^[15] Early computer science was strongly influenced by the work of mathematicians such as Kurt Gödel, Alan Turing, Rózsa Péter and Alonzo Church and there continues to be a useful interchange of ideas between the two fields in areas such as mathematical logic, category theory, domain theory, and algebra.^[19]

The relationship between Computer Science and Software Engineering is a contentious issue, which is further muddied by disputes over what the term 'Software Engineering' means, and how computer science is defined.^[43]David Parnas, taking a cue from the relationship between other engineering and science disciplines, has claimed that the principal focus of computer science is studying the properties of computation in general, while the principal focus of software engineering is the design of specific computations to achieve practical goals, making the two separate but complementary disciplines.^[44]

The academic, political, and funding aspects of computer science tend to depend on whether a department formed with a mathematical emphasis or with an engineering emphasis. Computer science departments with a mathematics emphasis and with a numerical orientation consider alignment with computational science. Both types of departments tend to make efforts to bridge the field educationally if not across all research.

Philosophy[edit]

A number of computer scientists have argued for the distinction of three separate paradigms in computer science. Peter Wegner argued that those paradigms are science, technology, and mathematics.^[45]Peter Denning's working group argued that they are theory, abstraction (modeling), and design.^[46] Amnon H. Eden described them as the 'rationalist paradigm' (which treats computer science as a branch of mathematics, which is prevalent in theoretical computer science, and mainly employs deductive reasoning), the 'technocratic paradigm' (which might be found in engineering approaches, most prominently in software engineering), and the 'scientific paradigm' (which approaches computer-related artifacts from the empirical perspective of natural sciences, identifiable in some branches of artificial intelligence).^[47]

Fields[edit]

Computer science is no more about computers than astronomy is about telescopes.

As a discipline, computer science spans a range of topics from theoretical studies of algorithms and the limits of computation to the practical issues of implementing computing systems in hardware and software.^[48][49]CSAB, formerly called Computing Sciences Accreditation Board—which is made up of representatives of the Association for Computing Machinery (ACM), and the IEEE Computer Society (IEEE CS)^[50]—identifies four areas that it considers crucial to the discipline of computer science: theory of computation, algorithms and data structures, programming methodology and languages, and computer elements and architecture. In addition to these four areas, CSAB also identifies fields such as software engineering, artificial intelligence, computer networking and communication, database systems, parallel computation, distributed computation, human–computer interaction, computer graphics, operating systems, and numerical and symbolic computation as being important areas of computer science.^[48]

Theoretical computer science[edit]

Theoretical Computer Science is mathematical and abstract in spirit, but it derives its motivation from practical and everyday computation. Its aim is to understand the nature of computation and, as a consequence of this understanding, provide more efficient methodologies. All studies related to mathematical, logic and formal concepts and methods could be considered as theoretical computer science, provided that the motivation is clearly drawn from the field of computing.

Data structures and algorithms[edit]

Data structures and algorithms are the study of commonly used computational methods and their computational efficiency.

Theory of computation[edit]

According to Peter Denning, the fundamental question underlying computer science is, 'What can be (efficiently) automated?'^[15] Theory of computation is focused on answering fundamental questions about what can be computed and what amount of resources are required to perform those computations. In an effort to answer the first question, computability theory examines which computational problems are solvable on various theoretical models of computation. The second question is addressed by computational complexity theory, which studies the time and space costs associated with different approaches to solving a multitude of computational problems.

The famous P = NP? problem, one of the Millennium Prize Problems,^[51] is an open problem in the theory of computation.

Information and coding theory[edit]

Information theory is related to the quantification of information. This was developed by Claude Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and communicating data.^[52]Coding theory is the study of the properties of codes (systems for converting information from one form to another) and their fitness for a specific application. Codes are used for data compression, cryptography, error detection and correction, and more recently also for network coding. Codes are studied for the purpose of designing efficient and reliable data transmission methods.^[53]

Programming language theory[edit]

Programming language theory is a branch of computer science that deals with the design, implementation, analysis, characterization, and classification of programming languages and their individual features. It falls within the discipline of computer science, both depending on and affecting mathematics, software engineering, and linguistics. It is an active research area, with numerous dedicated academic journals.

Formal methods[edit]

Formal methods are a particular kind of mathematically based technique for the specification, development and verification of software and hardware systems.^[54] The use of formal methods for software and hardware design is motivated by the expectation that, as in other engineering disciplines, performing appropriate mathematical analysis can contribute to the reliability and robustness of a design. They form an important theoretical underpinning for software engineering, especially where safety or security is involved. Formal methods are a useful adjunct to software testing since they help avoid errors and can also give a framework for testing. For industrial use, tool support is required. However, the high cost of using formal methods means that they are usually only used in the development of high-integrity and life-critical systems, where safety or security is of utmost importance. Formal methods are best described as the application of a fairly broad variety of theoretical computer science fundamentals, in particular logic calculi, formal languages, automata theory, and program semantics, but also type systems and algebraic data types to problems in software and hardware specification and verification.

Computer systems[edit]

Computer architecture and computer engineering[edit]

Computer architecture, or digital computer organization, is the conceptual design and fundamental operational structure of a computer system. It focuses largely on the way by which the central processing unit performs internally and accesses addresses in memory.^[55] The field often involves disciplines of computer engineering and electrical engineering, selecting and interconnecting hardware components to create computers that meet functional, performance, and cost goals.

Computer performance analysis[edit]

Computer performance analysis is the study of work flowing through computers with the general goals of improving throughput, controlling response time, using resources efficiently, eliminating bottlenecks, and predicting performance under anticipated peak loads.^[56]Benchmarks are used to compare the performance of systems carrying different chips and/or system architectures.^[57]

Concurrent, parallel and distributed systems[edit]

Concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other.^[58] A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi and the Parallel Random Access Machine model.^[59] When multiple computers are connected in a network while using concurrency, this is known as a distributed system. Computers within that distributed system have their own private memory, and information can be exchanged to achieve common goals.^[60]

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Computer networks[edit]

This branch of computer science aims to manage networks between computers worldwide.

Computer security and cryptography[edit]

Computer security is a branch of computer technology with an objective of protecting information from unauthorized access, disruption, or modification while maintaining the accessibility and usability of the system for its intended users. Cryptography is the practice and study of hiding (encryption) and therefore deciphering (decryption) information. Modern cryptography is largely related to computer science, for many encryption and decryption algorithms are based on their computational complexity.

Databases[edit]

A database is intended to organize, store, and retrieve large amounts of data easily. Digital databases are managed using database management systems to store, create, maintain, and search data, through database models and query languages.

Computer applications[edit]

Computer graphics and visualization[edit]

Computer graphics is the study of digital visual contents and involves the synthesis and manipulation of image data. The study is connected to many other fields in computer science, including computer vision, image processing, and computational geometry, and is heavily applied in the fields of special effects and video games.

Human–computer interaction[edit]

Research that develops theories, principles, and guidelines for user interface designers, so they can create satisfactory user experiences with desktop, laptop, and mobile devices.

Scientific computing[edit]

Scientific computing (or computational science) is the field of study concerned with constructing mathematical models and quantitative analysis techniques and using computers to analyze and solve scientific problems. In practical use, it is typically the application of computer simulation and other forms of computation to problems in various scientific disciplines.

Artificial intelligence[edit]

Artificial intelligence (AI) aims to or is required to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, learning, and communication found in humans and animals. From its origins in cybernetics and in the Dartmouth Conference (1956), artificial intelligence research has been necessarily cross-disciplinary, drawing on areas of expertise such as applied mathematics, symbolic logic, semiotics, electrical engineering, philosophy of mind, neurophysiology, and social intelligence. AI is associated in the popular mind with robotic development, but the main field of practical application has been as an embedded component in areas of software development, which require computational understanding. The starting point in the late 1940s was Alan Turing's question 'Can computers think?', and the question remains effectively unanswered, although the Turing test is still used to assess computer output on the scale of human intelligence. But the automation of evaluative and predictive tasks has been increasingly successful as a substitute for human monitoring and intervention in domains of computer application involving complex real-world data.

Software engineering[edit]

Software engineering is the study of designing, implementing, and modifying software in order to ensure it is of high quality, affordable, maintainable, and fast to build. It is a systematic approach to software design, involving the application of engineering practices to software. Software engineering deals with the organizing and analyzing of software—it doesn't just deal with the creation or manufacture of new software, but its internal maintenance and arrangement.

Discoveries[edit]

The philosopher of computing Bill Rapaport noted three Great Insights of Computer Science:^[61]

• Gottfried Wilhelm Leibniz's, George Boole's, Alan Turing's, Claude Shannon's, and Samuel Morse's insight: there are only two objects that a computer has to deal with in order to represent 'anything'.^{[note 4]}

All the information about any computable problem can be represented using only 0 and 1 (or any other bistable pair that can flip-flop between two easily distinguishable states, such as 'on/off', 'magnetized/de-magnetized', 'high-voltage/low-voltage', etc.).

• Alan Turing's insight: there are only five actions that a computer has to perform in order to do 'anything'.

• move left one location;
• move right one location;
• read symbol at current location;
• print 0 at current location;
• print 1 at current location.

• Corrado Böhm and Giuseppe Jacopini's insight: there are only three ways of combining these actions (into more complex ones) that are needed in order for a computer to do 'anything'.^[63]

• sequence: first do this, then do that;
• selection: IF such-and-such is the case, THEN do this, ELSE do that;
• repetition: WHILE such-and-such is the case DO this.

Programming paradigms[edit]

Programming languages can be used to accomplish different tasks in different ways. Common programming paradigms include:

• Functional programming, a style of building the structure and elements of computer programs that treats computation as the evaluation of mathematical functions and avoids state and mutable data. It is a declarative programming paradigm, which means programming is done with expressions or declarations instead of statements.^[64]
• Imperative programming, a programming paradigm that uses statements that change a program's state.^[65] In much the same way that the imperative mood in natural languages expresses commands, an imperative program consists of commands for the computer to perform. Imperative programming focuses on describing how a program operates.
• Object-oriented programming, a programming paradigm based on the concept of 'objects', which may contain data, in the form of fields, often known as attributes; and code, in the form of procedures, often known as methods. A feature of objects is that an object's procedures can access and often modify the data fields of the object with which they are associated. Thus Object-oriented computer programs are made out of objects that interact with one another.^[66]

Many languages offer support for multiple paradigms, making the distinction more a matter of style than of technical capabilities.^[67]

Academia[edit]

Conferences are important events for computer science research. During these conferences, researchers from the public and private sectors present their recent work and meet. Unlike in most other academic fields, in computer science, the prestige of conference papers is greater than that of journal publications.^[68][69] One proposed explanation for this is the quick development of this relatively new field requires rapid review and distribution of results, a task better handled by conferences than by journals.^[70]

Education[edit]

Computer Science, known by its near synonyms, Computing, Computer Studies, Information Technology (IT) and Information and Computing Technology (ICT), has been taught in UK schools since the days of batch processing, mark sensitive cards and paper tape but usually to a select few students.^[71] In 1981, the BBC produced a micro-computer and classroom network and Computer Studies became common for GCE O level students (11–16-year-old), and Computer Science to A level students. Its importance was recognised, and it became a compulsory part of the National Curriculum, for Key Stage 3 & 4. In September 2014 it became an entitlement for all 7,000,000 pupils over the age of 4.^[72]

In the US, with 14,000 school districts deciding the curriculum, provision was fractured.^[73] According to a 2010 report by the Association for Computing Machinery (ACM) and Computer Science Teachers Association (CSTA), only 14 out of 50 states have adopted significant education standards for high school computer science.^[74]

Israel, New Zealand, and South Korea have included computer science in their national secondary education curricula,^[75][76] and several others are following.^[77]

Challenges[edit]

In many countries, there is a significant gender gap in computer science education. In 2012, only 20 percent of computer science degrees in the United States were awarded to women.^[78] The gender gap is also a problem in other western countries.^[79] The gap is smaller, or nonexistent, in some parts of the world. In 2011, women earned half of the computer science degrees in Malaysia.^[80] In 2001, 55 percent of computer science graduates in Guyana were women.^[79]

See also[edit]

Computer science – Wikipedia book

Notes[edit]

1. ^In 1851
2. ^'The introduction of punched cards into the new engine was important not only as a more convenient form of control than the drums, or because programs could now be of unlimited extent, and could be stored and repeated without the danger of introducing errors in setting the machine by hand; it was important also because it served to crystallize Babbage's feeling that he had invented something really new, something much more than a sophisticated calculating machine.' Bruce Collier, 1970
3. ^See the entry'Computer science' on Wikiquote for the history of this quotation.
4. ^The word 'anything' is written in quotation marks because there are things that computers cannot do. One example is: to answer the question if an arbitrary given computer program will eventually finish or run forever (the Halting problem).

References[edit]

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Further reading[edit]

Overview[edit]

• Tucker, Allen B. (2004). Computer Science Handbook (2nd ed.). Chapman and Hall/CRC. ISBN978-1-58488-360-9.
  • 'Within more than 70 chapters, every one new or significantly revised, one can find any kind of information and references about computer science one can imagine. […] all in all, there is absolute nothing about Computer Science that can not be found in the 2.5 kilogram-encyclopaedia with its 110 survey articles […].' (Christoph Meinel, Zentralblatt MATH)
• van Leeuwen, Jan (1994). Handbook of Theoretical Computer Science. The MIT Press. ISBN978-0-262-72020-5.
  • '[…] this set is the most unique and possibly the most useful to the [theoretical computer science] community, in support both of teaching and research […]. The books can be used by anyone wanting simply to gain an understanding of one of these areas, or by someone desiring to be in research in a topic, or by instructors wishing to find timely information on a subject they are teaching outside their major areas of expertise.' (Rocky Ross, SIGACT News)
• Ralston, Anthony; Reilly, Edwin D.; Hemmendinger, David (2000). Encyclopedia of Computer Science (4th ed.). Grove's Dictionaries. ISBN978-1-56159-248-7.
  • 'Since 1976, this has been the definitive reference work on computer, computing, and computer science. […] Alphabetically arranged and classified into broad subject areas, the entries cover hardware, computer systems, information and data, software, the mathematics of computing, theory of computation, methodologies, applications, and computing milieu. The editors have done a commendable job of blending historical perspective and practical reference information. The encyclopedia remains essential for most public and academic library reference collections.' (Joe Accardin, Northeastern Illinois Univ., Chicago)
• Edwin D. Reilly (2003). Milestones in Computer Science and Information Technology. Greenwood Publishing Group. ISBN978-1-57356-521-9.

Selected literature[edit]

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• Knuth, Donald E. (1996). Selected Papers on Computer Science. CSLI Publications, Cambridge University Press.
• Collier, Bruce (1990). The little engine that could've: The calculating machines of Charles Babbage. Garland Publishing Inc. ISBN978-0-8240-0043-1.
• Cohen, Bernard (2000). Howard Aiken, Portrait of a computer pioneer. The MIT press. ISBN978-0-262-53179-5.
• Tedre, Matti (2014). The Science of Computing: Shaping a Discipline. CRC Press, Taylor & Francis.
• Randell, Brian (1973). The origins of Digital computers, Selected Papers. Springer-Verlag. ISBN978-3-540-06169-4.
  • 'Covering a period from 1966 to 1993, its interest lies not only in the content of each of these papers – still timely today – but also in their being put together so that ideas expressed at different times complement each other nicely.' (N. Bernard, Zentralblatt MATH)

Articles[edit]

• Peter J. Denning. Is computer science science?, Communications of the ACM, April 2005.
• Peter J. Denning, Great principles in computing curricula, Technical Symposium on Computer Science Education, 2004.
• Research evaluation for computer science, Informatics Europe report. Shorter journal version: Bertrand Meyer, Christine Choppy, Jan van Leeuwen and Jorgen Staunstrup, Research evaluation for computer science, in Communications of the ACM, vol. 52, no. 4, pp. 31–34, April 2009.

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Curriculum and classification[edit]

• Association for Computing Machinery. 1998 ACM Computing Classification System. 1998.
• Joint Task Force of Association for Computing Machinery (ACM), Association for Information Systems (AIS) and IEEE Computer Society (IEEE CS). Computing Curricula 2005: The Overview Report. September 30, 2005.
• Norman Gibbs, Allen Tucker. 'A model curriculum for a liberal arts degree in computer science'. Communications of the ACM, Volume 29 Issue 3, March 1986.

External links[edit]

• Computer science at Curlie
• Photographs of computer scientists by Bertrand Meyer

Bibliography and academic search engines[edit]

Introduction To Computer Science Pdf

• CiteSeer^x (article): search engine, digital library and repository for scientific and academic papers with a focus on computer and information science.
• DBLP Computer Science Bibliography (article): computer science bibliography website hosted at Universität Trier, in Germany.
• The Collection of Computer Science Bibliographies (article)

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Professional organizations[edit]

Misc[edit]

• Computer Science—Stack Exchange: a community-run question-and-answer site for computer science

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