Which world most important algorithms have contributed most to humankind in past decades?
I thought this is a good general knowledge for a developer to know about.
Update:
If possible, please keep the answer to a specific programming algorithm.
I would like to get a list of the most important ones, only one algorithm per answer.
Please consider to state why the algorithm is significant and important...
Public/private key encryption is pretty darn important. Internet commerce would be nowhere as ubiquitous without it.
The algorithm exist in every router in the world, for identifying the best route between two nodes in a network.
The FFT is an extremely important and widely-used method of extracting useful information from sampled signals.
A fast Fourier transform (FFT) is an efficient algorithm to compute the discrete Fourier transform (DFT) and its inverse.
PageRank is a link analysis algorithm, named after Larry Page, used by the Google Internet search engine that assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of "measuring" its relative importance within the set.
In computer science and information theory, data compression or source coding is the process of encoding information using fewer bits (or other information-bearing units) than an unencoded representation would use, through use of specific encoding schemes.
This may be too far fetched so please comment.
I think one of such examples is the Smith-Waterman and Needleman-Wunsch algorithms and their approximations. All of them are essentially doing the same thing: they align two or more strings (sequences). There is a significance in Biology. When DNA or Protein sequences are aligned - regions of structural, functional and evolutionary similarity are revealed.
A heuristic that approximates Smith-Waterman is BLAST. It allows searching sequences of large databases for Biological similarity. The popularity of BLAST is really great - it's very likely the most widely used algorithm in Biology. The newer areas in Bioinformatics and Genomics have newer and better approximations of Smith-Waterman/Needleman-Wunsch algorithms that are more accurate than BLAST.
High-throughput approximations of Smith-Waterman and Needleman-Wunsch that are faster than BLAST are used to assemble Genomes from shotgun sequencing - where the product of the sequencer machine is a huge amount DNA reads (billions) from arbitrary parts of the Genome that are very short (50 to 100 nucleotides). The approach was used to complete the Human Genome Project. All modern sequencing is done this way.
Numerous Multiple Sequence Alignment algorithms exist - they are approximating a multi-sequence version of the Smith-Waterman/Needleman-Wunsch. Multiple sequences are aligned as a group simultaneously to each other. It's a much harder problem than it's pairwise counterpart, but the solutions provide much more insight into Biological function, structure, and evolutionary history of related sequences.
Siam named the following as the most important algorithms of the 20th century:
1946: The Metropolis Algorithm for Monte Carlo. Through the use of random processes, this algorithm offers an efficient way to stumble toward answers to problems that are too complicated to solve exactly.
1947: Simplex Method for Linear Programming. An elegant solution to a common problem in planning and decision-making.
1950: Krylov Subspace Iteration Method. A technique for rapidly solving the linear equations that abound in scientific computation.
1951: The Decompositional Approach to Matrix Computations. A suite of techniques for numerical linear algebra.
1957: The Fortran Optimizing Compiler. Turns high-level code into efficient computer-readable code.
1959: QR Algorithm for Computing Eigenvalues. Another crucial matrix operation made swift and practical.
1962: Quicksort Algorithms for Sorting. For the efficient handling of large databases.
1965: Fast Fourier Transform. Perhaps the most ubiquitous algorithm in use today, it breaks down waveforms (like sound) into periodic components.
1977: Integer Relation Detection. A fast method for spotting simple equations satisfied by collections of seemingly unrelated numbers.
1987: Fast Multipole Method. A breakthrough in dealing with the complexity of n-body calculations, applied in problems ranging from celestial mechanics to protein folding.
Personally I would replace Integer Relation Detection with PageRank.
PageRank, love it or hate it, but it affects decisions and actions of millions people worldwide googling on a daily basis.
If I had to list the top 3 most important algorithms in use today in computers, I would say:
The Binary Search algorithm is used constantly to narrow down on an item within a sorted list, most index lookups will use something along these lines at some point. This algorithm provides a search of an ordered list in o(log n) time.
The Quicksort algorithm finally managed to get sort down to O(n log n) average case and O(n^2) worse case. Sorting is one of the most common data tasks in a computer and one of the most expensive, improving the average case sort was a huge step forward in efficiency.
Dijkstra's algorithm as has been said produces a shortest path between points within a graph. This is used widely for all manner of routing applications, most extensively with regard to the internet itself ensuring that the quickest path through the tangled web of interconnected routers is used.
It has probably contributed the most to keeping the amount of time-wasting spam in my inbox to a manageable level.
Of course, I'm it has been used in numerous other worthwhile applications, but SPAM-killing is my favorite.
This is the sorting algorithm now used in Python, Java 7 and Android
Basically:
N-1 exactly on already sorted list)And the beauty of it ? It's stable! And therefore suitable for multipass sorting according to various criterion.
By the way, if anyone has an optimized C++ implementation on hand...
All algorithms which used for solving Visibility Problem in 3D Computer Animation seems crucial to me.
The painter's algorithm, also known as a priority fill, is one of the simplest solutions to the visibility problem in 3D computer graphics. When projecting a 3D scene onto a 2D plane, it is necessary at some point to decide which polygons are visible, and which are hidden.
In computer graphics, z-buffering is the management of image depth coordinates in three-dimensional (3-D) graphics, usually done in hardware, sometimes in software. It is one solution to the visibility problem, which is the problem of deciding which elements of a rendered scene are visible, and which are hidden. The painter's algorithm is another common solution which, though less efficient, can also handle non-opaque scene elements. Z-buffering is also known as depth buffering.
In 3D computer graphics, hidden surface determination (also known as hidden surface removal (HSR), occlusion culling (OC) or visible surface determination (VSD) is the process used to determine which surfaces and parts of surfaces are not visible from a certain viewpoint. A hidden surface determination algorithm is a solution to the visibility problem, which was one of the first major problems in the field of 3D computer graphics. The process of hidden surface determination is sometimes called hiding, and such an algorithm is sometimes called a hider. The analogue for line rendering is hidden line removal. Hidden surface determination is necessary to render an image correctly, so that one cannot look through walls in virtual reality, for example.
Whichever one you need to solve your current problem.
Originally used to decode convolutional error-correcting codes, it is now used to solve a wide class of recognition problems (ranging from speech recognition to bioinformatics). You can find it in several communication and storage devices.
Although it is a more general term than a specific algorithm, I'd mention MP3 as the aggregation of the different algorithms and techiques which work in cooperation to generate this lossy audio format.
It has surely been very significant in the "digital era".
Runge-Kutta numerical integration. Without it many simulations would not be possible. No space program, no nuclear power, no ballistics, no sports simulations, no bullet proof vests, no crash test simulation, no fluid motion simulation, no chemical interaction simulations, no earthquake resistant buildings ... the list goes on.
Sorting algorithm.
Insertion Sort
Easy to implement, very fast on small lists and used in Merge Sort / Quicksort implementations to speed them up. It's stable, and operates in O(n) on sorted lists (when sorted in ascending order).
It gets heavily used in navigation, target tracking (for almost any sensor: radar, sonar, FLIR, ladar). One textbook shows an application in a disk drive controller. Robotic control systems frequently use Kalman filters.
Spoken and Written language.
They are currently one of the most efficient algorithm to transfer knowledge from one thing to another. Without language, civil society could not exist, and information could not be conveyed.
The heap data structure and its associated algorithms for heap construction and maintenance.
And show some respect for quicksort. Even if it is not always the sort of choice, it is one of the fundamental algorithms in the historical development of computer science, and is a great vehicle for understanding recursion and algorithm analysis. It is beautiful, and yes, I love it.
Indexing algorithms like B-tree,B+-tree,hash index,binary tree index etc. To index huge amount of data.
MapReduce as a way to divide, conquer and parallelize processing of large data sets.
Brute Force Algorithm !
Many people underestimate this brute force algorithm. Actually it is mostly used to solve problems having no pattern. I love it much!
Bubble Sort !
Bubble sort is not as bad as Bogosort. That is why I vote for the Bubble sort.