Difference between revisions of "Algorithm"
Karl Jones (Talk | contribs) (Mental model) |
Karl Jones (Talk | contribs) (→See also) |
||
| Line 30: | Line 30: | ||
* [[Function (mathematics)]] | * [[Function (mathematics)]] | ||
* [[Kruskal's algorithm]] | * [[Kruskal's algorithm]] | ||
| + | * [[Mathematical model]] | ||
| + | * [[Mathematicals]] | ||
* [[Maze generation algorithm]] | * [[Maze generation algorithm]] | ||
* [[Mental model]] | * [[Mental model]] | ||
Revision as of 09:20, 29 August 2015
In mathematics and computer science, an algorithm (Listeni/ˈælɡərɪðəm/ al-gə-ri-dhəm) is a self-contained step-by-step set of operations to be performed.
Algorithms exist that perform calculation, data processing, and automated reasoning.
An algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function.
Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing "output" and terminating at a final ending state.
The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.
The concept of algorithm has existed for centuries, however a partial formalization of what would become the modern algorithm began with attempts to solve the Entscheidungsproblem (the "decision problem") posed by David Hilbert in 1928.
Subsequent formalizations were framed as attempts to define "effective calculability" or "effective method"; those formalizations included:
- The Gödel–Herbrand–Kleene recursive functions of 1930, 1934 and 1935
- Alonzo Church's lambda calculus of 1936
- Emil Post's "Formulation 1" of 1936
- Alan Turing's Turing machines of 1936–7 and 1939.
Giving a formal definition of algorithms, corresponding to the intuitive notion, remains a challenging problem.
Algorithm analysis is an important part of a broader computational complexity theory, which provides theoretical estimates for the resources needed by any algorithm which solves a given computational problem. These estimates provide an insight into reasonable directions of search for efficient algorithms.
See also
- Algorithm design
- Analysis of algorithms
- Combinatorics
- Entscheidungsproblem
- Function (mathematics)
- Kruskal's algorithm
- Mathematical model
- Mathematicals
- Maze generation algorithm
- Mental model
- Signal processing
External links
- Algorithm @ Wikipedia
