Using Memoization to Speed Up Code

# What is memoization?

Yeah, that's right, memo, not memor. I asked my self this when I first saw it. Simply put, memoization can be described as the caching of the results of a sub-step within an algorithm.

That makes it perfect for recursive functions, as they waste plenty of time recomputing everything, so that's what I tried it on, a recursive function.

And what recursive function is better known than a function to calculate the Fibonacci sequence?

After learning about memoization and how to do it, I spent hours perfecting a memoization wrapper which I then used on a JavaScript bigInt Fibonacci function.

I then laughed at the speed differences.

I calculated 1000 iterations of the Fibonacci sequence in 1 second. Without it, it took forever. You can try any number less than 200,000 without choosing to loop, and you'll receive an answer within 10 seconds, guaranteed or I'll refund you.

Try it out yourself!
https://MemoizedFibonacci.studentfires.repl.run

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DynamicSquid (4532)

@StudentFires Yeah, now that I think about it, there isn't anything else you could be referring to when you said "variable argument functions". So anyway, I wrote this example:

``````#include <iostream>
using namespace std;

template<typename Result, typename ValueType>
void Sum(Result& result, ValueType& valueType)
{
result += valueType;
}

template<typename Result, typename Current, typename... Next> // variadic template
void Sum(Result& result, Current currentValue, Next... valueN) // make sure the two functions have the same name
{
result += currentValue;
return Sum(result, valueN...);
}

int main()
{