From 6e1e045773bc1dbea4841d88ca8ca01631cefb23 Mon Sep 17 00:00:00 2001
From: lerethel <32931827+lerethel@users.noreply.github.com>
Date: Tue, 8 Oct 2024 14:43:12 +0300
Subject: [PATCH] Update 101-big-theta.md (#7364)
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* Update 101-big-theta.md

Replace 'Theta' with 'θ' where applicable and remove unnecessary escaping

* Update 101-big-theta.md

Remove unnecessary parentheses
---
 .../103-asymptotic-notation/101-big-theta.md                    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

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.