# Understanding Big O  URL:: https://brilliant.org/courses/computer-science-algorithms/the-speed-of-algorithms-2/more-big-o/2/ Author:: is a ## Highlights > we described the runtime of selection sort as being “**in**” O(n2)O(n^2)O(n2). This is because, mathematically, O(n2)O(n^2)O(n2) is a *set* of functions. Every polynomial that has n2n^2n2 as its largest polynomial term is in O(n2),O(n^2),O(n2), and there are an infinite number of functions in O(n2)O(n^2)O(n2). ([View Highlight](https://read.readwise.io/read/01ga7f9ka3ykt3jj9ya8r1agxk)) --- Title: Understanding Big O Author: is a Tags: readwise, articles date: 2024-01-30 --- # Understanding Big O  URL:: https://brilliant.org/courses/computer-science-algorithms/the-speed-of-algorithms-2/more-big-o/2/ Author:: is a ## AI-Generated Summary In the last lesson, you might have noticed an unusual turn of phrase: we described the runtime of selection sort as being “in” O(n2)O(n^2)O(n2). ## Highlights > we described the runtime of selection sort as being “**in**” O(n2)O(n^2)O(n2). This is because, mathematically, O(n2)O(n^2)O(n2) is a *set* of functions. Every polynomial that has n2n^2n2 as its largest polynomial term is in O(n2),O(n^2),O(n2), and there are an infinite number of functions in O(n2)O(n^2)O(n2). ([View Highlight](https://read.readwise.io/read/01ga7f9ka3ykt3jj9ya8r1agxk))