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The algorithm generates a random permutation of its input using a quantum source of entropy, checks if the list is sorted, and, if it is not, destroys the universe. Assuming that the many-worlds interpretation holds, the use of this algorithm will result in at least one surviving universe where the input was successfully sorted in O( n ) time.
In computer science, multiply-with-carry (MWC) is a method invented by George Marsaglia [1] for generating sequences of random integers based on an initial set from two to many thousands of randomly chosen seed values.
Widely used in many programs, e.g. it is used in Excel 2003 and later versions for the Excel function RAND [8] and it was the default generator in the language Python up to version 2.2. [9] Rule 30: 1983 S. Wolfram [10] Based on cellular automata. Inversive congruential generator (ICG) 1986 J. Eichenauer and J. Lehn [11] Blum Blum Shub: 1986
Numba is used from Python, as a tool (enabled by adding a decorator to relevant Python code), a JIT compiler that translates a subset of Python and NumPy code into fast machine code. Pythran compiles a subset of Python 3 to C++ . [165] RPython can be compiled to C, and is used to build the PyPy interpreter of Python.
For example, in a massively parallel application, each thread or GPU core can be assigned a range of counter values and compute random numbers without synchronization or shared state. Since the generator does not require stepping through every intermediate state, it can “jump” to any point in the sequence in constant time.
A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random determined by the random bits; thus either the running time, or the output (or both) are ...
The Lehmer random number generator [1] (named after D. H. Lehmer), sometimes also referred to as the Park–Miller random number generator (after Stephen K. Park and Keith W. Miller), is a type of linear congruential generator (LCG) that operates in multiplicative group of integers modulo n. The general formula is
Moreover, C++11 allows foreach loops to be applied to any class that provides the begin and end functions. It's then possible to write generator-like classes by defining both the iterable methods (begin and end) and the iterator methods (operator!=, operator++ and operator*) in the same class. For example, it is possible to write the following ...