A Gentle Introduction to Algorithm Complexity Analysis

The explanation of Big O in Grokking Algorithms was clearest for me. It doesn't assume any knowledge and even re-introduces logarithms and binary search before using them in examples: - Logarithms and binary search: https://livebook.manning.com/#!/book/grokking-algorithms/cha... - Big O: https://livebook.manning.com/#!/book/grokking-algorithms/cha...

For anyone interested in this subject I'd highly recommend the Algorithm Design Manual by Steven Skiena[0]. He goes over this in pretty good detail in the first ~1/4 of the book. This was my first introduction to Algorithm Complexity Analysis and by the end of that section I already found myself looking at my code in a different light. [0] http://www.amazon.com/dp/1849967202

A handy tip to reverse engineer complexity from empirical examples is plot on a log-log plot and measure the gradient (if its a straight line). The gradient is the exponent, ie. O(n^gradient). Great if your algorithm is so complex you can't analyze it. http://www.dgp.toronto.edu/people/JamesStewart/378notes/05lo...