Random Number Generators

Random number generation

Random number generation is a procedure that, typically by means of a random number generator (RNG) or a random sequence of numbers or symbols that cannot be easily predicted better than by random chance is generated. This means that the outcome sequence will contain some patterns detectable in hindsight but impossible to see in the future. It is true that random number generators can be hardware random-number generators(HRNGS) that produce random numbers, and each generation is a result of the present value of a physical environment's attribute , which can change in a way that's difficult to predict. 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.

Various applications of randomness have led the way to the development of a variety of methods for creating random data. Some of these have existed from the beginning of time, there are many popular "classic" examples, including the rolling of dice, coin flipping, the shuffling of playing cards, the usage of yarrow plants (for the purpose of divination) inside the I Ching, as well as a myriad of other techniques. Due to the mechanical nature of these methods the creation of large quantities of random numbers (important in statistics) needed a lot of effort and time. This meant that the results might be collected and distributed as random number tables.

There are a variety of computational methods that generate pseudorandom numbers exist. They all fall short of the notion of real randomness, though they do meet, with varying results, certain tests that are that measure how random their results are (that is in what extent their patterns can be discerned). This makes them ineffective to be used in applications such as encryption. However, specially designed cryptographically secure pseudorandom number generators (CSPRNGS) are also are available, featuring functions specifically designed to be used in cryptography.

Practical applications and uses [editPractical applications and uses[edit

Original article Uses to randomness

Random number generators have applications to games of chance, statistical sampling and computer simulation, cryptography, completely randomized design, and other areas that produce an unpredictable outcome is desired. Generallyspeaking, in applications with unpredictability as the paramount feature like security applications, hardware generators are generally preferred over pseudorandom algorithmswhen feasible.

Pseudorandom generators are beneficial in the development of Monte Carlo-method simulations since they allow for debugging. This is made easier due to the capability to run the identical pattern of random numbers again using this same random seed. They can also be used in cryptography so long as their seed remains secret. Both receivers and senders can generate the same set of numbers to use as keys.

The generation of pseudorandom numbers is an important and common task in computer programming. While cryptography and certain numerical algorithms require a very significant amount of apparent randomness, many other applications require the slightest amount of uncertainty. Simple examples include showing a user an "random quote of the day", or determining which way an opponent controlled by computers will move during a computer game. A less pronounced form of randomness is utilized in hash algorithms, as well as in the development of amortized searching or sorting techniques.

Certain applications that appear at initially to be appropriate for randomization may not be necessarily so easy to use. For instance, a device that "randomly" selects music tracks for a background music system is only able to look random. It might even allow for control over the musical selections in a truly random system. would have no restriction on the same music track appearing several times in succession.