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Chudnovsky algorithm. The Chudnovsky algorithm is a fast method for calculating the digits of π, based on Ramanujan 's π formulae. Published by the Chudnovsky brothers in 1988, [ 1] it was used to calculate π to a billion decimal places. [ 2]
In computing, floating-point arithmetic ( FP) is arithmetic that represents subsets of real numbers using an integer with a fixed precision, called the significand, scaled by an integer exponent of a fixed base. Numbers of this form are called floating-point numbers. [1] : 3 [2] : 10 For example, 12.345 is a floating-point number in base ten ...
With the 52 bits of the fraction (F) significand appearing in the memory format, the total precision is therefore 53 bits (approximately 16 decimal digits, 53 log 10 (2) ≈ 15.955). The bits are laid out as follows: The real value assumed by a given 64-bit double-precision datum with a given biased exponent and a 52-bit fraction is
Two's complement is the most common method of representing signed (positive, negative, and zero) integers on computers, and more generally, fixed point binary values. Two's complement uses the binary digit with the greatest value as the sign to indicate whether the binary number is positive or negative; when the most significant bit is 1 the number is signed as negative and when the most ...
Single-precision floating-point format (sometimes called FP32 or float32) is a computer number format, usually occupying 32 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point . A floating-point variable can represent a wider range of numbers than a fixed-point variable of the same bit ...
In computing, half precision (sometimes called FP16 or float16) is a binary floating-point computer number format that occupies 16 bits (two bytes in modern computers) in computer memory. It is intended for storage of floating-point values in applications where higher precision is not essential, in particular image processing and neural ...
Bailey–Borwein–Plouffe formula. The Bailey–Borwein–Plouffe formula ( BBP formula) is a formula for π. It was discovered in 1995 by Simon Plouffe and is named after the authors of the article in which it was published, David H. Bailey, Peter Borwein, and Plouffe. [1] Before that, it had been published by Plouffe on his own site. [2]
In computing, a roundoff error, [1] also called rounding error, [2] is the difference between the result produced by a given algorithm using exact arithmetic and the result produced by the same algorithm using finite-precision, rounded arithmetic. [3] Rounding errors are due to inexactness in the representation of real numbers and the ...