Editing Exponential growth (section)

===Computer science===
* [[Clock rate|Processing power]] of computers. See also [[Moore's law]] and [[technological singularity]]. (Under exponential growth, there are no singularities. The singularity here is a metaphor, meant to convey an unimaginable future. The link of this hypothetical concept with exponential growth is most vocally made by futurist [[Raymond Kurzweil|Ray Kurzweil]].)
* In [[computational complexity theory]], computer algorithms of exponential complexity require an exponentially increasing amount of resources (e.g. time, computer memory) for only a constant increase in problem size. So for an algorithm of time complexity {{math|2<sup>''x''</sup>}}, if a problem of size {{math|1=''x'' = 10}} requires 10 seconds to complete, and a problem of size {{math|1=''x'' = 11}} requires 20 seconds, then a problem of size {{math|1=''x'' = 12}} will require 40 seconds. This kind of algorithm typically becomes unusable at very small problem sizes, often between 30 and 100 items (most computer algorithms need to be able to solve much larger problems, up to tens of thousands or even millions of items in reasonable times, something that would be physically impossible with an exponential algorithm). Also, the effects of [[Moore's Law]] do not help the situation much because doubling processor speed merely increases the feasible problem size by a constant. E.g. if a slow processor can solve problems of size {{mvar|x}} in time {{mvar|t}}, then a processor twice as fast could only solve problems of size {{math|''x'' + constant}} in the same time {{mvar|t}}. So exponentially complex algorithms are most often impractical, and the search for more efficient algorithms is one of the central goals of computer science today.