From f5a92cd682d7c0b37d476bb7be17105be8cf5e5c Mon Sep 17 00:00:00 2001
From: Enlightenus <88682809+Enlightenus@users.noreply.github.com>
Date: Tue, 30 Jul 2024 00:03:29 -0700
Subject: [PATCH] Update time_complexity.md

Refine the first paragraph to keep the definition of time complexity
---
 en/docs/chapter_computational_complexity/time_complexity.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/en/docs/chapter_computational_complexity/time_complexity.md b/en/docs/chapter_computational_complexity/time_complexity.md
index c634b04a6..caf910a41 100644
--- a/en/docs/chapter_computational_complexity/time_complexity.md
+++ b/en/docs/chapter_computational_complexity/time_complexity.md
@@ -1,6 +1,6 @@
 # Time complexity
-
-We can directly and accurately assess the efficiency an algorithm by measuring the run time. How to accurately assess the run time of an algorithm?
+Time complexity describes the run time of an algorithm as a function of the size of the input data.
+The direct way to assess the efficiency of an algorithm is measuring the run time. How to accurately measure the run time of an algorithm?
 
 1. **Determining the Running Platform**: This includes hardware configuration, programming language, system environment, etc., all of which can affect the efficiency of code execution.
 2. **Evaluating the Run Time for Various Computational Operations**: For instance, an addition operation `+` might take 1 ns, a multiplication operation `*` might take 10 ns, a print operation `print()` might take 5 ns, etc.