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Learn Big O Notation once and for all
- Big O notation is used to classify algorithms according to how their run time or space requirements grow as the input size grows.
- O(n) notation is meant to analyze algorithms that take linearly more time as the size of input increases. This can be seen in simple examples like finding the maximum element of an array.
- O(1) notation represents algorithms that take constant time to solve a problem, regardless of the size of input. Examples include finding the first element of an array or appending items to an array.
- O(n^2) notation represents algorithms that take quadratic time to solve a problem, which can be seen when we have to traverse two-dimensional arrays.
- O(n log n) notation is usually found in sorting algorithms and implies a combination of a logarithmic and linear runtime complexity.
- O(2^n) notation represents algorithms with an exponential complexity, commonly found in recursive algorithms that branch out in two ways.
- O(n!) notation represents algorithms that require factorial time complexity, which is extremely rare and inefficient.
- Understanding Big O notation is crucial for analyzing and improving algorithms, especially in job interviews or coding challenges.
- The cheatsheet provided by Eric Rowell can be a helpful tool for visualizing the different complexity notations and comparing their growth rates.
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