The concept of interval notation comes up in both Mathematics and Computer Science. The Mathematical notation [, ], (, ) denotes the domain (or range) of an interval.
The brackets [ and ] means:
The parenthesis ( and ) means:
An interval with mixed states is called "half-open".
For example, the range of consecutive integers from 1 .. 10 (inclusive) would be notated as such:
Notice how the word inclusive was used. If we want to exclude the end point but "cover" the same range we need to move the end-point:
For both left and right edges of the interval there are actually 4 permutations:
(1,10) = 2,3,4,5,6,7,8,9 Set has 8 elements
(1,10] = 2,3,4,5,6,7,8,9,10 Set has 9 elements
[1,10) = 1,2,3,4,5,6,7,8,9 Set has 9 elements
[1,10] = 1,2,3,4,5,6,7,8,9,10 Set has 10 elements
How does this relate to Mathematics and Computer Science?
Array indexes tend to use a different offset depending on which field are you in:
These differences can lead to subtle fence post errors, aka, off-by-one bugs when implementing Mathematical algorithms such as for-loops.
If we have a set or array, say of the first few primes [ 2, 3, 5, 7, 11, 13, 17, 19, 23, 29 ], Mathematicians would refer to the first element as the 1st absolute element. i.e. Using subscript notation to denote the index:
Some programming languages, in contradistinction, would refer to the first element as the zero'th relative element.
Since the array indexes are in the range [0,N-1] then for clarity purposes it would be "nice" to keep the same numerical value for the range 0 .. N instead of adding textual noise such as a -1 bias.
For example, in C or JavaScript, to iterate over an array of N elements a programmer would write the common idiom of i = 0, i < N with the interval [0,N) instead of the slightly more verbose [0,N-1]:
function main() {_x000D_
var output = "";_x000D_
var a = [ 2, 3, 5, 7, 11, 13, 17, 19, 23, 29 ];_x000D_
for( var i = 0; i < 10; i++ ) // [0,10)_x000D_
output += "[" + i + "]: " + a[i] + "\n";_x000D_
_x000D_
if (typeof window === 'undefined') // Node command line_x000D_
console.log( output )_x000D_
else_x000D_
document.getElementById('output1').innerHTML = output;_x000D_
} <html>_x000D_
<body onload="main();">_x000D_
<pre id="output1"></pre>_x000D_
</body>_x000D_
</html>Mathematicians, since they start counting at 1, would instead use the i = 1, i <= N nomenclature but now we need to correct the array offset in a zero-based language.
e.g.
function main() {_x000D_
var output = "";_x000D_
var a = [ 2, 3, 5, 7, 11, 13, 17, 19, 23, 29 ];_x000D_
for( var i = 1; i <= 10; i++ ) // [1,10]_x000D_
output += "[" + i + "]: " + a[i-1] + "\n";_x000D_
_x000D_
if (typeof window === 'undefined') // Node command line_x000D_
console.log( output )_x000D_
else_x000D_
document.getElementById( "output2" ).innerHTML = output;_x000D_
}<html>_x000D_
<body onload="main()";>_x000D_
<pre id="output2"></pre>_x000D_
</body>_x000D_
</html>Aside:
In programming languages that are 0-based you might need a kludge of a dummy zero'th element to use a Mathematical 1-based algorithm. e.g. Python Index Start
Interval notation is also important for floating-point numbers to avoid subtle bugs.
When dealing with floating-point numbers especially in Computer Graphics (color conversion, computational geometry, animation easing/blending, etc.) often times normalized numbers are used. That is, numbers between 0.0 and 1.0.
It is important to know the edge cases if the endpoints are inclusive or exclusive:
Where M is some machine epsilon. This is why you might sometimes see const float EPSILON = 1e-# idiom in C code (such as 1e-6) for a 32-bit floating point number. This SO question Does EPSILON guarantee anything? has some preliminary details. For a more comprehensive answer see FLT_EPSILON and David Goldberg's What Every Computer Scientist Should Know About Floating-Point Arithmetic
Some implementations of a random number generator, random() may produce values in the range 0.0 .. 0.999... instead of the more convenient 0.0 .. 1.0. Proper comments in the code will document this as [0.0,1.0) or [0.0,1.0] so there is no ambiguity as to the usage.
Example:
random() colors. You convert three floating-point values to unsigned 8-bit values to generate a 24-bit pixel with red, green, and blue channels respectively. Depending on the interval output by random() you may end up with near-white (254,254,254) or white (255,255,255). +--------+-----+
|random()|Byte |
|--------|-----|
|0.999...| 254 | <-- error introduced
|1.0 | 255 |
+--------+-----+
For more details about floating-point precision and robustness with intervals see Christer Ericson's Real-Time Collision Detection, Chapter 11 Numerical Robustness, Section 11.3 Robust Floating-Point Usage.