If you're up for a challenge…
The classic Fibonacci implementation is
def fib(0), 0
def fib(1), 1
def fib(n), fib(n-1) + fib(n-2)This has a worst case performance of \( O( 1.6^{n+1} ) \). For large \( n \), this is a big number (20 digits or so long for \( n = 100 \)). This means it is slow, and it burns through stack space at an alarming rate.
However, you can make the function perform in linear time by
implementing a simple cache. An Elixir map is a good data structure
for this. Initialize it with %{ 0 => 0, 1 => 1 }, and add new values
to it as you discover them.
Write a module that implements the cache. It should use an agent, and that agent's state should be the map.
Then write a Fibonacci module that uses this cache.
Try calculating fib(100). If it comes back without crashing your
machine, and before the sun has expanded to consume the Earth, then
your cache worked...
This is kind of a simple solution. First thing I came up with; I need to look over some of the other solutions to see what I missed.
[NB] Guess I missed the point about having a separate module for the cache...
iex(7)> Fib.find 100
354224848179261915075
iex(8)> Fib.find 1000
43466557686937456435688527675040625802564660517371780402481729089536555417949051890403879840079255169295922593080322634775209689623239873322471161642996440906533187938298969649928516003704476137795166849228875
iex(9)> Fib.find 10000
336447648764317832666216120051075433103021484606800639065647699746800814421666623681555955136337340255820... . . .
642530711298441182622661057163515069260029861704945425047491378115154139941550671256271197133252763631939606902895650288268608362241082050562430701794976171121233066073310059947366875
iex(110)>