Most of our rounding methods are based on arithmetic rounding.

Please refer to the nomenclature described in Residue Class Rounding. So we can base our rounding on a metric and the most obvious choice is the absolute value of the difference of the two parameters, . That is what everybody does and nothing else really comes to our mind when we are talking about rounding. But this approach is based on the additive structure of our field or ring containing the numbers that we want to round. Another way of looking at this is that (in case we are talking of real numbers or some subset thereof) the choice of the rounded value is mostly based on seeing on which side of a boundary between the two candidates for the rounded values the original value lies, with special handling if it is exactly equal to this boundary. Now the this boundary is the arithmetic mean between the two candidates, therefore we call this arithmetic rounding. Just to give you an example:

We look at the „middle“ between two allowed numbers, for example usually something like when rounding a number between and to integers and automatically assume that above that middle we round up, below we round down and exactly on the boundary we need additional rules. This middle is the arithmetic mean of the two neighboring allowed numbers after rounding and hence we can call this arithmetic rounding. All numbers with are rounded to two, all numbers with are rounded to three.

# Means

We assume and , so each real number should be rounded to an integral number. Also we assume .

Now assume we have a non-integral number and an integral number such . For convenience we write . Then we round to if and to if , so serves as boundary. Now there are many other ways to define a mean between two number and . Some of the most common are

- arithmetic mean:
- geometric mean:
- harmonic mean:
- quadratic mean:
- cubic mean:
- generalized mean:
- arithmetic-geometric mean: Let
Then we have the AGM

- harmonic-geometric mean (also called geometric-harmonic mean): Can be defined in a similar way, by just replacing the artihmetic mean by the harmonic mean.
- contraharmonic mean is not so hard to understand, find it in Wikipedia.
- logarithmic mean is not so hard to understand, find it also in Wikipedia.

We could also define a arithmetic-harmonic mean in a similar way, but that is just the geometric mean. It is an interesting way to approach the geometric mean of matrices, which is somewhat hard to define otherwise because of the non-commutative multiplication.

For the users of spreadsheets, like included in LibreOffice, OpenOffice or MS-Office, you can find the arithmetic mean as function `average`

, the geometric mean as `geomean`

and the harmonic mean as `harmean`

.

Others means can be defined along these lines. And we can see that:

Here is a visual explanation of arithmetic, geometric and harmonic mean:

The line from K to L has the harmonic mean of and as a length. The geometric mean would be reached by moving that line to a position that the upper and lower part have the same shape, apart from scaling. And the arithmetic mean would be reached by moving K and L to the middle points of the lines AD and BC, respectively.

The ruby-gem long-decimal supports all of these methods for calculating a mean.

# Using means to define rounding methods

Just to give you an idea that you can extend this further, the geometric and harmonic mean are now considered.

As can be seen negative and zero values for and are causing some trouble or require some care. Actually we can deal with this by splitting our unrounded and rounded worlds into separate parts, here we take the number , the and the numbers as three different sets and do the rounding within them:

with

where we define

and

and

In a similar way we define , and and define metrics , , separately. Then we just look at one partition and consider rounding to be a mapping

such that is minimal.

The obvious assumption of rounding to releaves us from dealing with logarithms of zero or division by zero. We would see that anyway any number between and would be rounded to for both harmonic and geometric rounding, because the boundary is 0, if we pick the right variant of the formula for calculating the mean. Also between and everything is rounded to in our example. Otherwise the calculation of the boundary can be made to work, because then both factors are negative, so we draw the square root of a positive product, but then we need to replace the square root by its negative counterpart.

So for geometric rounding we define that is rounded to if , rounded to if and rounding for has to be defined by additional rules. But we define for negative values of and to be . This can also be expressed in terms of a metric:

and

Harmonic rounding is definable in a similar way:

We define that is rounded to if , rounded to if and rounding for has to be defined by additional rules. This can also be expressed in terms of a metric:

and

Quadratic and Cubic rounding can be defined in a similar way. It would even be possible to go for arithmetic-geometric rounding or harmonic-geometric rounding, but that would imply having to do an approximative iterative calculation of the boundary each time it is needed, while the rounding modes described above can still be achieved with relatively simple multiplication and addition operations.

In any case it is necessary to define the rule how to round numbers that lie exactly on the boundary.

The ruby-gem long-decimal supports geometric, harmonic, quadratic and cubic rounding in addition to the regular arithmetic rounding, that is referred to as „half“.

Harmonic and geometric rounding are important for some of the mechanisms of rounding with sum.

Based on this basis these algorithms will look more natural and no longer like some weird approach that needs to be copied from some untrusted source just to get over the nightmare of having to solve that issue.

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