1.8.1 Asymptotic Notations Big Oh - Omega - Theta #1 | Highlights and Annotations by Gistr.

ASMIC notation uses Big O (upper bound), Big Omega (lower bound), and Theta (average bound) to represent function growth in algorithms. Big O is most useful; if a precise bound (Theta) isn't possible, Big O or Big Omega provide approximations. The choice of notation depends on the function's growth rate, aiming for the closest representation. These notations are unrelated to best/worst-case algorithm analysis. This segment contrasts Big O notation with Omega notation, emphasizing the change in inequality and its implications for identifying lower bounds. The use of the same example as in the Big O segment allows for a direct comparison and strengthens understanding of the relationship between upper and lower bounds. This segment provides a clear explanation of the Big O notation's formal definition, clarifying the conditions for a function f(n) to be O(g(n)). The explanation is concise and focuses on the core concept of upper bounds, making it easily understandable for viewers.This segment uses a practical example (f(n) = 2n + 3) to demonstrate how to apply the Big O notation. It breaks down the process step-by-step, highlighting the selection of appropriate constants and the reasoning behind choosing a suitable g(n), making the abstract concept more concrete and accessible.