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Rounding Algorithms
dtmos writes "Clive Maxfield has an interesting article up on PL DesignLine cataloging most (all?) of the known rounding algorithms used in computer math. As he states, "...the mind soon boggles at the variety and intricacies of the rounding algorithms that may be used for different applications ... round-up, round-down, round-toward-nearest, arithmetic rounding, round-half-up, round-half-down, round-half-even, round-half-odd, round-toward-zero, round-away-from-zero, round-ceiling, round-floor, truncation (chopping), round-alternate, and round-random (stochastic rounding), to name but a few." It's a good read, especially if you *think* you know what your programs are doing."
especially if you *think* you know what your programs are doing.
Pfft... I've been writing programs and working with computers for over 25 years. I *STILL* haven't figured out what they are doing. Come to think of it, I could say the samething about my wife.
If "disco" means "I learn" in Latin, does "discothèque" mean "I learn technology"?
Almost correct, but I think it can be simplified to the following:
quidquid latine dictum sit altum videtur.
-5.8 --> -5.8+0.5 --> -5.3 --> truncate(-5.3) = -5.0
which is not what you want.
In c++, using std::floor will give the correct results with this method though
-5.8 --> -5.8+0.5 --> -5.3 --> floor(-5.3) = -6.0 (correct)
whereas :
-5.3 --> -5.3+0.5 --> -4.8 --> floor(-4.8) = -5.0 (correct)
This stuff is still important. Yes the big computers we have on our desks can do high precision floating point. but there are still some very small 4-bit and 8-bit micro controllers that controll battery chargers, control motors that move antenna on spacecraft and the control fins on air to air missles. And then there are those low-end DSP chips inside TV sets and digital cameras and camcorders.... These controllers need to do complex math using short integers and how round off errors accumulate still does matter. Remember: Not all software runs on PCs in fact _most_ software does not.
For illustration, suppose we use a floating-point format with a 10-bit mantissa. For a fixed exponent (say 0), this can represent values from 1.0 to 1 1023/1024, in 1/1024 increments. The AVERAGE of these UNROUNDED values is 1 1023/2048, which is LESS THAN 1.5. However, if all these values are rounded (with 0.5 rounding up), the AVERAGE of the ROUNDED values will be EQUAL TO 1.5, an average increase of 1/2048. Thus, this type of rounding introduces a measurable positive bias into the arithmetic.
No, the "bias" came from your choice of data (and your unrealistic expectation that the average of a set of rounded values would equal the average of the unrounded set).
Such examples are as easy to construct as they are misleading. Suppose we instead take the values {0.2, 0.3, and 0.5}. Their average is 1/3, and if we round them ".5 up" we wind up with {0,0,1} with exactly the same average. On the other hand, if we round them with ".5 to even" we wind up with {0,0,0} with the average of zero and an "error" (by your metric) of 100%.
--MarkusQ