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In particular, IEEE 754 already uses "canonical NaN" with the meaning of "canonical encoding of a NaN" (e.g. "isCanonical(x) is true if and only if x is a finite number, infinity, or NaN that is canonical." page 38, but also for totalOrder page 42), thus a different meaning from what is used here. Please help clarify the section.
NaN is treated as if it had a larger absolute value than Infinity (or any other floating-point numbers). (−NaN < −Infinity; +Infinity < +NaN.) qNaN and sNaN are treated as if qNaN had a larger absolute value than sNaN. (−qNaN < −sNaN; +sNaN < +qNaN.) NaN is then sorted according to the payload.
The standard also defines representations for positive and negative infinity, a "negative zero", five exceptions to handle invalid results like division by zero, special values called NaNs for representing those exceptions, denormal numbers to represent numbers smaller than shown above, and four rounding modes.
Excel maintains 15 figures in its numbers, but they are not always accurate; mathematically, the bottom line should be the same as the top line, in 'fp-math' the step '1 + 1/9000' leads to a rounding up as the first bit of the 14 bit tail '10111000110010' of the mantissa falling off the table when adding 1 is a '1', this up-rounding is not undone when subtracting the 1 again, since there is no ...
the theoretical real numbers (an extended number line) the entities which can be represented in the format (a finite set of numbers, together with −0, infinities, and NaN) the particular representations of the entities: sign-exponent-significand, etc. the bit-pattern (encoding) used.
If an IEEE 754 single-precision number is converted to a decimal string with at least 9 significant digits, and then converted back to single-precision representation, the final result must match the original number. [6] The sign bit determines the sign of the number, which is the sign of the significand as well. "1" stands for negative.
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largest subnormal number 0 00001 0000000000: 0400: 2 −14 × (1 + 0 / 1024 ) ≈ 0.00006103515625: smallest positive normal number 0 01101 0101010101: 3555: 2 −2 × (1 + 341 / 1024 ) ≈ 0.33325195: nearest value to 1/3 0 01110 1111111111: 3bff: 2 −1 × (1 + 1023 / 1024 ) ≈ 0.99951172: largest number less than one 0 ...