INTRODUCTION

What is Time Complexity?

Time complexity is a measurement of how long an algorithm takes to run with respect to the size of the input. It calculates the correlation between the size of the input and the runtime of an algorithm. Big O notation is frequently used to represent time complexity and offers an upper limit on how quickly an algorithm can expand.

Why is it helpful?

• Determines the effectiveness and scalability of an algorithm.
• • It helps us to compare several algorithms and determine which is more useful in a certain circumstance.
• • By carefully taking into account temporal complexity, we may choose, improve, and tune the performance of algorithms..
• It also predicts performance problems that may arise as the input size rises.

How to calculate and what to ignore and why to ignore?

The temporal complexity of an algorithm is assessed by comparing the quantity of input to the number of operations. However, we are more interested in how the runtime grows as the input size grows than the specific number of operations.

When calculating the temporal complexity of an algorithm, we often neglect constant factors and lower-order components and concentrate on the dominating term that has the greatest influence on the growth rate. This is due to the fact that the dominant term will obscure the impact of other terms for bigger inputs.