Time Complexity
View on GitHubTime Complexity for Competitive Programming
Welcome to the session "Time Complexity for Competitive Programming" session. This session is designed to help beginner competitive programmers understand the time complexity, which is crucial for writing efficient code. Below, you'll find a brief overview of the key concepts covered in the session.
Key Concepts
1. Theoretical Side of Code Efficiency
In this section, we discuss why code efficiency is important in competitive programming. Efficient code can mean the difference between solving a problem within the given time limits or facing a time-out error.
2. Asymptotic Notation
Asymptotic notation provides a way to describe the behavior of functions as they grow towards infinity. It helps in analyzing the efficiency of algorithms, especially when dealing with large inputs.
3. Big O Notation
Big O notation is used to classify algorithms according to how their run time or space requirements grow as the input size grows. It's a crucial concept for understanding the upper bounds of an algorithm's running time.
Scientific Notation of Numbers
Understanding the scientific notation of numbers is essential for dealing with large numbers frequently encountered in competitive programming. It simplifies the representation and manipulation of these numbers.
Time Complexity Examples
In this section, we solve various examples to illustrate how to calculate the time complexity of different algorithms. These examples help solidify the theoretical concepts discussed and provide practical insights into their application.
My Session @ ICPC SCU
You can watch the recorded session on Time Complexity for Competitive Programming by following this link to my YouTube Channel.