# The Power of Math

In a couple of previous posts I was considering which is better in Python: map or list comprehension. The final idea was that none of them is as fast as a C++ program, even using exactly the same algorithm.

What about changing an algorithm a little bit?

### The Idea

I was thinking about speeding up the Python code. Then I thought about the simple equation for calculating nth Fibonacci number. It is very simple, the algorithm complexity is not that bad, it is something like a^{n} + b^{n}. This is much faster for bigger `n`

then the recursive/iterative version.

If I was going to sum all the numbers from 1 to n, then I wouldn’t write this:

```
def sum_numbers(n):
return sum([x for x in range(1, n+1)])
```

I’d rather use the quite simple equation for summing it:

`sum(n) = (1 + n) * n / 2`

Then the program looks like:

```
def sum_numbers(n):
return (1 + n) * n / 2
```

The time difference is huge: `n=10*1000*1000`

: first program: `5s`

, second program: `15ms`

. So that’s just `333`

times faster.

### The Solution

So what about the equation for calculating the `sum of all squares of even numbers from 1 to 1,000,000`

?. It is very simple:

`sum(n) = (4n³ + 6n² + 2n)/3`

So let’s implement it and check the time:

```
def sum_numbers(n):
return (4 * n**3 + 6 * n**2 + 2*n) / 3
```

This runs in `10ms`

. Much better compared to the previous solution, which took `70s`

.

### The Final Conclusion

Sometimes the best solution is brute force. Sometimes it’s just the good old math.