The commonest floating point formats are those defined in IEEE 754 however there ere others before this Floating-point expansions are another way to get a greater precision, benefiting from the floating-point hardware: a number is represented as an unevaluated sum of several floating-point numbers. There are several ways to represent floating point number but IEEE 754 is the most efficient in most cases. 次の表は、各浮動小数点型の仮数と指数に割り当てられたビット数を示します。 Normal exponents range from … It is intended for applications where it is necessary to emulate decimal rounding exactly, such as financial and tax computations. In computing, decimal64 is a decimal floating-point computer numbering format that occupies 8 bytes (64 bits) in computer memory. The exponent of a 64 bit floating point comprises 11 bits. Interpreted as ae The smallest floating point number depends on the number format used and whether sub-normals are supported. IEEE Standard 754 floating point is the most common representation today for real numbers on computers, including Intel-based PC’s, Macs, and most Unix platforms. A subnormal number is defined in IEEE Std 754 -2008, section 2.1.51, as a non-zero floating point number with magnitude less than the magnitude of that formats smallest normal number… Your book is wrong, but not with regard to the largest representable floating point value. It ignores the denormalized numbers. A floating-point number is said to be normalized if the most significant digit of the mantissa is 1. Normal exponents range from -1022 to 1023. Floating-point variables are represented by a mantissa, which contains the value of the number, and an exponent, which contains the order of magnitude of the number. The problem is the smallest. @user5414: In IEEE-754 64-bit binary floating-point, subnormal numbers all use 2**-1022. An example is double-double arithmetic , sometimes used for the C type long double . 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