Next: , Previous: , Up: Floating Point in Depth   [Contents][Index]

### 28.9 Significance Loss

A much more serious source of error in floating-point computation is significance loss from subtraction of nearly equal values. This means that the number of bits in the significand of the result is fewer than the size of the value would permit. If the values being subtracted are close enough, but still not equal, a single subtraction can wipe out all correct digits, possibly contaminating all future computations.

Floating-point calculations can sometimes be carefully designed so that significance loss is not possible, such as summing a series where all terms have the same sign. For example, the Taylor series expansions of the trigonometric and hyperbolic sines have terms of identical magnitude, of the general form ```x**(2*n + 1) / (2*n + 1)!```. However, those in the trigonometric sine series alternate in sign, while those in the hyperbolic sine series are all positive. Here is the output of two small programs that sum k terms of the series for `sin (x)`, and compare the computed sums with known-to-be-accurate library functions:

```x = 10      k = 51
s (x)   = -0.544_021_110_889_270
sin (x) = -0.544_021_110_889_370

x = 20      k = 81
s (x)   = 0.912_945_250_749_573
sin (x) = 0.912_945_250_727_628

x = 30      k = 109
s (x)   = -0.987_813_746_058_855
sin (x) = -0.988_031_624_092_862

x = 40      k = 137
s (x)   = 0.617_400_430_980_474
sin (x) = 0.745_113_160_479_349

x = 50      k = 159
s (x)   = 57_105.187_673_745_720_532
sin (x) = -0.262_374_853_703_929

// sinh(x) series summation with positive signs
// with k terms needed to converge to machine precision

x = 10      k = 47
t (x)    = 1.101_323_287_470_340e+04
sinh (x) = 1.101_323_287_470_339e+04

x = 20      k = 69
t (x)    = 2.425_825_977_048_951e+08
sinh (x) = 2.425_825_977_048_951e+08

x = 30      k = 87
t (x)    = 5.343_237_290_762_229e+12
sinh (x) = 5.343_237_290_762_231e+12

x = 40      k = 105
t (x)    = 1.176_926_334_185_100e+17
sinh (x) = 1.176_926_334_185_100e+17

x = 50      k = 121
t (x)    = 2.592_352_764_293_534e+21
sinh (x) = 2.592_352_764_293_536e+21
```

The `sinh (x)` series with positive terms can be summed to high accuracy. By contrast, the series for `sin (x)` suffers increasing significance loss, so that when x = 30 only two correct digits remain. Soon after, all digits are wrong, and the answers are complete nonsense.
An important skill in numerical programming is to recognize when significance loss is likely to contaminate a computation, and revise the algorithm to reduce this problem. Sometimes, the only practical way to do so is to compute in higher intermediate precision, which is why the extended types like `long double` are important.