Random Number Generators

Random number generation

Random number generation is a method by which, usually through the use of a random number generator (RNG) it is a sequence of numbers or symbols that are not able to be predicted better than by random chance is generated. This means that the resultant sequence may contain patterns that can be seen in hindsight, but unpredictable to the foresight. Truly random number generators can be hardware random-number generators(HRNGS) that generate random numbers. Each generation is a result of the current value of the physical environment's characteristic that can change in a manner that's practically impossible to model. This would be in contrast to so-called "random number generations" done by pseudorandom number generators (PRNGs) that generate numbers that only look random but are in fact pre-determined--these generations can be reproduced simply by knowing the state of the PRNG.

Many applications of randomness has led to the development diverse methods of generating random data. Certain of these techniques have been used since the beginning of time, and among whose ranks are popular "classic" examples, including the rolling of dice, coin flipping, the shuffling of cards, the usage of yarrow plants (for for divination) found in the I Ching, as well as a myriad of other techniques. Due to their mechanical character of techniques the creation of large quantities of numbers that were sufficiently random (important in statistics) required lots of work and effort. This meant that the results might be compiled and distributed in random number tables.

Numerous computational methods to generate pseudorandom numbers exist. All fall short of the aim of true randomness. However, they could be able to meet, but with different effectiveness, some of the tests that are designed to determine how random their results are (that is, to what degree their patterns are discernible). These tests are usually not appropriate for use in applications like Cryptography. However, carefully designed security-assured cryptographically secured pseudorandom generators (CSPRNGS) are also exist, equipped with specific functions specifically designed to be used in cryptography.

Practical applications and uses [edit]

The main article is: Application of randomness

Random number generators may be utilized in the gambling industry, statistical sampling computers, statistical sampling, cryptography completely randomized design and other areas that produce an unpredictable outcome is desirable. Most often, in cases that require unpredictable outcomes as their primary feature, such as in security, hardware generators are generally preferred over pseudorandom algorithm, if feasible.

Pseudorandom generators are useful when developing Monte Carlo methods of simulations, as debugging is made easier due to the ability to run the same number of random sequences again by starting from exactly the same random seed. They also are used for cryptography, so long that their seed is secret. Sender and receiver can generate the same number of numbers to use as keys.

The generation of pseudorandom numbers is a critical and routine task in computer programming. While cryptography as well as certain numerical algorithms require a very significant amount of apparent randomness, many other operations only need a modest amount of unpredictability. Simple examples include presenting a user with the "random quote of the day", or determining which way an adversary controlled by computers could move during a computer game. A less pronounced form of randomness is employed in hash algorithms and when creating amortized-searching or sorting techniques.

Some programs that appear at first sight to be suitable for randomization may not be very simple. For instance, a system which "randomly" selects music tracks to play as background music should only look random. It could even contain ways to regulate the musical selections the system has no limit on having the same track appear more than three times in succession.

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