python-ds-algo-notes.txt

Kadane's Algorithm - Find Maximum Sub-Array
Floyds Cycle Finding Algorithm - Detect Loop in a Linked List

Time Complexity - lang and meth to describe efficiency of algos
  time complexity increases when input increases

  Asymptotic Analysis/Bounds
    Big-O - Worst Case - less or equal to the worst case, (Maximum time)
    Big-Omega - Best Case Scenario - at least more than the best case, (Best Time)
    Big-Theta - Average Case Scenario - within bounds of the worst and best case scenarios (Average Time)

    o(1) - constant
    o(n) - linear
    o(LogN) - Logarithmic
    o(N2) - Quadratic
    o(2n) - Exponential

  Space Complexity
    Amount of space needed for algorithm to Recursion
    It's represented the same way as time complexity


Recursion - When a method calls itself
  3 steps
    1. Recursive Case
    2. Base case - the stopping criterion
    3. Unintentional case - the constraint