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24 changes: 15 additions & 9 deletions concepts/big-o-notation.md
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Expand Up @@ -96,6 +96,21 @@ Since we can do this for all sloped lines, they fall under the same Big O.

## Algorithm Time

### Constant Time

Imagine a racer that can teleport. That means that it would take the same amount of time for them to travel any distance. We call this runtime `O(1)` or **constant time**.

This Big O covers ALL teleportation, whether it takes 1 second or 1 million years, since the time is constant, even if the distance is variable.


### Logarithmic Time

Imagine a spaceship that doubles it speed with every passing unit of distance. This behavior is the inverse of the the Exponential Algorithm, and is represented by `O(log n)`.

### Linear Time

When in the worst case scenario, you need to traverse the whole sequence to calculate the operation. For example finding an element in an unsorted list (find in array).

### Quadratic Time

An algo with a loop is considered `O(n²)`, which is **quadratic**. A quadratic algo may seem faster than a linear algo in the beginning, but eventually the linear algo becomes faster. Even a performance boost cannot make the quadratic algo faster, as distance increases.
Expand All @@ -106,15 +121,6 @@ We group together all algos with the form `O(nᵏ)` under the term **Polynomial

Some algos repeat all previous steps before making the next one. These are considered `O(2ⁿ)`, and are VERY slow.

### Constant Time

Imagine a racer that can teleport. That means that it would take the same amount of time for them to travel any distance. We call this runtime `O(1)` or **constant time**.

This Big O covers ALL teleportation, whether it takes 1 second or 1 million years, since the time is constant, even if the distance is variable.

### Logarithmic Time

Imagine a spaceship that doubles it speed with every passing unit of distance. This behavior is the inverse of the the Exponential Algorithm, and is represented by `O(log n)`.

## Input Type

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