# Useful Formulae

In this lesson, we'll study some mathematical formulae that would make calculating time complexity easier!

We'll cover the following

## Formulae

Here is a list of handy formulas which can be helpful when calculating the Time Complexity of an algorithm:



Summation
Equation
$\left(\sum_{i=1}^n c \right) = c + c+ c + \cdots + c$
$cn$
$\left(\sum_{i=1}^n i \right) = 1+2+3+\cdots+n$
$\frac{n(n+1)}{2}$
$\left(\sum_{i=1}^n i^2 \right) = 1 + 4 + 9 + \cdots + n^2$
$\frac{n(n+1)(2n+1)}{6}$
$\left(\sum_{i=0}^n r^i\right) = r^0 + r^1 + r^2 + \cdots + r^n$
$\frac{(r^{n+1}-1)}{r-1}$

## General Tips

1. Every time a list or array gets iterated over $c \times length$ times, it is most likely in $O(n)$ time.
2. When you see a problem where the number of elements in the problem space gets halved each time, it will most probably be in $O(log n)$ runtime.
3. Whenever you have a single nested loop, the problem is most likely in quadratic time.

In the next lesson, we will discuss some common scenarios and how you can calculate their complexities.

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