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 Fri November 15th 2002 You are here: home > some page I haven't put in the locator thingy yet
cf_fraction v. 1.0 algorithm
Method by Dr. Peterson, implementation by Michel Vuijlsteke

Here's the method used in <cf_fraction>, with a partial explanation for how it works:

We want to approximate a value m (given as a decimal) between 0 and 1, by a fraction Y/X. Think of fractions as vectors (denominator, numerator), so that the slope of the vector is the value of the fraction. We are then looking for a lattice vector (X, Y) whose slope is as close as possible to m. This picture illustrates the goal, and shows that, given two vectors A and B on opposite sides of the desired slope, their sum A + B = C is a new vector whose slope is between the two, allowing us to narrow our search:

```num
^
|
+  +  +  +  +  +  +  +  +  +  +
|
+  +  +  +  +  +  +  +  +  +  +
|                                  slope m=0.7
+  +  +  +  +  +  +  +  +  +  +   /
|                               /
+  +  +  +  +  +  +  +  +  +  D <--- solution
|                           /
+  +  +  +  +  +  +  +  + /+  +
|                       /
+  +  +  +  +  +  +  C/ +  +  +
|                   /
+  +  +  +  +  + /+  +  +  +  +
|              /
+  +  +  +  B/ +  +  +  +  +  +
|          /
+  +  + /A  +  +  +  +  +  +  +
|     /
+  +/ +  +  +  +  +  +  +  +  +
| /
+--+--+--+--+--+--+--+--+--+--+--> denom```

Here we start knowing the goal is between A = (3,2) and B = (4,3), and formed a new vector C = A + B. We test the slope of C and find that the desired slope m is between A and C, so we continue the search between A and C. We add A and C to get a new vector D = A + 2*B, which in this case is exactly right and gives us the answer.

Given the vectors A and B, with slope(A) < m < slope(B), we can find consecutive integers M and N such that slope(A + M*B) < x < slope(A + N*B) in this way:

If A = (b, a) and B = (d, c), with a/b < m < c/d, solve
 a + x*c = m b + x*d

to give
 x = b*m - a c - d*m

If this is an integer (or close enough to an integer to consider it so), then A + x*B is our answer. Otherwise, we round it down and up to get integer multipliers M and N respectively, from which new lower and upper bounds A' = A + M*B and B' = A + N*B can be obtained. Repeat the process until the slopes of the two vectors are close enough for the desired accuracy. The process can be started with vectors (0,1), with slope 0, and (1,1), with slope 1. Surprisingly, this process produces exactly what continued fractions produce, and therefore it will terminate at the desired fraction (in lowest terms, as far as I can tell) if there is one, or when it is correct within the accuracy of the original data.

For example, for the slope 0.7 shown in the picture above, we get these approximations:

Step 1: A = 0/1, B = 1/1 (a = 0, b = 1, c = 1, d = 1)
 x = 1 * 0.7 - 0 0.7 = 0.7 = 2.3333 1 - 1 * 0.7 0.3

M = 2: lower bound A' = (0 + 2*1) / (1 + 2*1) = 2 / 3
N = 3: upper bound B' = (0 + 3*1) / (1 + 3*1) = 3 / 4

Step 2: A = 2/3, B = 3/4 (a = 2, b = 3, c = 3, d = 4)
 x = 3 * 0.7 - 2 = 0.1 = 0.5 3 - 4 * 0.7 0.2

M = 0: lower bound A' = (2 + 0*3) / (3 + 0*4) = 2 / 3
N = 1: upper bound B' = (2 + 1*3) / (3 + 1*4) = 5 / 7 Step 3: A = 2/3, B = 5/7 (a = 2, b = 3, c = 5, d = 7)
 x = 3 * 0.7 - 2 = 0.1 = 1 5 - 7 * 0.7 0.1

N = 1: exact value A' = B' = (2 + 1*5) / (3 + 1*7) = 7 / 10

which of course is obviously right.

In most cases you will never get an exact integer, because of rounding errors, but can stop when one of the two fractions is equal to the goal to the given accuracy.