Computer Science Algorithm


  • Analysis of an algorithm's efficiency
  • Rate of growth of operations
    • How the runtime or space increases as the input increases
  • Big-O is a measurement of the worst case


  • Ignore constants
    • Values that don't scale with the input
  • Ignore low-order terms

Common Run Times

  • Fastest --> slowest
Big OName
$O(1)$constant/bounded time
$O(log ; n)$log time
$O(n)$linear time
$O(n ; log ; n)$$n ; log ; n$ time
$O(n^2)$quadratic/exponential time
$O(2^n)$exponential time
$O(n!)$factorial time