Fixed point number usually allow only 8 bits (32 bit computing) of binary numbers for the fractional portion of the number which means many decimal numbers are recorded inaccurately. Floating Point numbers use exponents to shift the decimal point therefore they can store more accurate fractional values than fixed point numbers. However the CPU will have to perform extra arithmetic to read the number when stored in this format. Fixed point number usually allow only 8 bits (32 bit computing) of binary numbers for the fractional portion of the number which means many decimal numbers are recorded inaccurately. Floating Point numbers use exponents to shift the decimal point therefore they can store more accurate fractional values than fixed point numbers. However the CPU will have to perform extra arithmetic to read the number when stored in this format.
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It's a tricky area: Decimal numbers can be represented exactly. In contrast, numbers like 1.1 do not have an exact representation in binary floating point. End users typically would not expect 1.1 to display as 1.1000000000000001 as it does with binary floating point. The exactness carries over into arithmetic. In decimal floating point, 0.1 + 0.1 + 0.1 - 0.3 is exactly equal to zero. In binary floating point, the result is 5.5511151231257827e-017. While near to zero, the differences prevent reliable equality testing and differences can accumulate. For this reason, decimal is preferred in accounting applications which have strict equality invariants. So you have to be carefull how you store floating point decimals in binary. It can also be used in a fraction. It must be simplufied then reduced and multiplied.
If you mean floating point number, they are significand, base and exponent.
the set of points equidistant from a fixed point
floating point operating per second
That is the Fulcrum.