diff --git a/src/data/roadmaps/datastructures-and-algorithms/content/104-algorithmic-complexity/103-asymptotic-notation/101-big-theta.md b/src/data/roadmaps/datastructures-and-algorithms/content/104-algorithmic-complexity/103-asymptotic-notation/101-big-theta.md index d683a72e5..b51840083 100644 --- a/src/data/roadmaps/datastructures-and-algorithms/content/104-algorithmic-complexity/103-asymptotic-notation/101-big-theta.md +++ b/src/data/roadmaps/datastructures-and-algorithms/content/104-algorithmic-complexity/103-asymptotic-notation/101-big-theta.md @@ -1,3 +1,3 @@ # Big-θ Notation -Big Theta \(\Theta\) notation is used in computer science to describe an asymptotic tight bound on a function. This essentially means it provides both an upper and lower bound for a function. When we say a function f(n) is \(\Theta(g(n))\), we mean that the growth rate of f(n) is both bounded above and below by the function g(n) after a certain point. This is more precise than Big O and Big Omega notation, which provide only an upper and a lower bound, respectively. Big Theta notation tells us exactly how a function behaves for large input values. For example, if an algorithm has a time complexity of \(\Theta(n^2)\), it means the running time will increase quadratically with the input size. \ No newline at end of file +Big Theta (θ) notation is used in computer science to describe an asymptotic tight bound on a function. This essentially means it provides both an upper and lower bound for a function. When we say a function f(n) is θ(g(n)), we mean that the growth rate of f(n) is both bounded above and below by the function g(n) after a certain point. This is more precise than Big O and Big Omega notation, which provide only an upper and a lower bound, respectively. Big Theta notation tells us exactly how a function behaves for large input values. For example, if an algorithm has a time complexity of θ(n^2), it means the running time will increase quadratically with the input size.