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Because modular exponentiation is an important operation in computer science, and there are efficient algorithms (see above) that are much faster than simply exponentiating and then taking the remainder, many programming languages and arbitrary-precision integer libraries have a dedicated function to perform modular exponentiation: Python's ...
x 1 = x; x 2 = x 2 for i = k - 2 to 0 do if n i = 0 then x 2 = x 1 * x 2; x 1 = x 1 2 else x 1 = x 1 * x 2; x 2 = x 2 2 return x 1 The algorithm performs a fixed sequence of operations ( up to log n ): a multiplication and squaring takes place for each bit in the exponent, regardless of the bit's specific value.
Range reduction (also argument reduction, domain-spltting) is the first step for any function, after checks for unusual values (infinity and NaN) are performed.The goal here is to reduce the domain of the argument for the polynomial to process, using the function's symmetry and periodicity (if any), setting flags to indicate e.g. whether to negate the result in the end (if needed).
Python supports normal floating point numbers, which are created when a dot is used in a literal (e.g. 1.1), when an integer and a floating point number are used in an expression, or as a result of some mathematical operations ("true division" via the / operator, or exponentiation with a negative exponent).
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.
[8] [9] In programming languages such as Ada, [10] Fortran, [11] Perl, [12] Python [13] and Ruby, [14] a double asterisk is used, so x 2 is written as x ** 2. The plus–minus sign , ±, is used as a shorthand notation for two expressions written as one, representing one expression with a plus sign, the other with a minus sign.
Here, complexity refers to the time complexity of performing computations on a multitape Turing machine. [1] See big O notation for an explanation of the notation used. Note: Due to the variety of multiplication algorithms, M ( n ) {\displaystyle M(n)} below stands in for the complexity of the chosen multiplication algorithm.
*/ /* This implementation does not implement composite functions, functions with a variable number of arguments, or unary operators. */ while there are tokens to be read: read a token if the token is: - a number: put it into the output queue - a function: push it onto the operator stack - an operator o 1: while ( there is an operator o 2 at the ...