Pain vs. Suffering

All growth comes with some degree of pain, as it pulls you out of your comfort zone. The greater the growth, the greater the pain. But pain in the service of growth is a good thing, as long as that pain is what’s necessary to achieve the growth that you’re aiming for. And even better than that, this pain is only temporary. It’s what will launch you forward into the next phase of your life.

Suffering is pain without purpose. Pain with no higher goal. Pain with no dreams, no ambition, no aspiration.

Big O Notation

Big O notation is used in Computer Science to describe the performance or complexity of an algorithm. Big O specifically describes the worst-case scenario, and can be used to describe the execution time required or the space used (e.g. in memory or on disk) by an algorithm.

O(1)

O(1) describes an algorithm that will always execute in the same time (or space) regardless of the size of the input data set.

O(N)

O(N) describes an algorithm whose performance will grow linearly and in direct proportion to the size of the input data set.

O(N²)

O(N²) represents an algorithm whose performance is directly proportional to the square of the size of the input data set. This is common with algorithms that involve nested iterations over the data set. Deeper nested iterations will result in O(N³), O(N⁴) etc.

O(2^N)

O(2^N) denotes an algorithm whose growth doubles with each addition to the input data set. The growth curve of an O(2^N) function is exponential — starting off very shallow, then rising meteorically.