reading-notes

Class 01

Pain vs. Suffering

Source: Pain vs. Suffering

• Pain = growth
• Necesarry to go out of your comfort zone to grow
• Pain =/= suffering
• Suffering = pain w/o purpose

When you feel pain, reflect on:

1. What’s your perspective?
2. Why are you doing this?
3. Do you want what comes at the end of this journey?
4. Are you doing this for you?

Big O

Source: A beginner’s guide to Big O notation

• Describes performance of complecity of an algorithm
• Big 0 = worst case scenario
• Also described execution time or space used

O(1) – executed in same time regardless of input data

bool IsFirstElementNull(IList<string> elements)
{
    return elements[0] == null;
}

O(N) – linear performance growth in proportion to dataset size

bool ContainsValue(IList<string> elements, string value)
{
    foreach (var element in elements)
    {
        if (element == value) return true;
    }

    return false;
}

O(N²) – proportional to the square of the size of input data

bool ContainsDuplicates(IList<string> elements)
{
    for (var outer = 0; outer < elements.Count; outer++)
    {
        for (var inner = 0; inner < elements.Count; inner++)
        {
            // Don't compare with self
            if (outer == inner) continue;

            if (elements[outer] == elements[inner]) return true;
        }
    }

    return false;
}

O(2^N) – Doubles with each addition to input (exponenial)

int Fibonacci(int number)
{
    if (number <= 1) return number;

    return Fibonacci(number - 2) + Fibonacci(number - 1);
}

Logarithms – Increasing has little affect

e.g. binary search 1/2 the set each search)