What is a Logarithm?
The power to which a base needs to be raised to reach a given number is called the logarithm of that number for the respective base.
For finding logarithmic two necessary factors that need to be known are base and number.
Examples:
logarithm of 8 for base 2 = log2(8) = 3,
Explanation: 23 = 8 Since 2 needs to be raised to a power of 3 to give 8, Thus logarithm of 8 base 2 is 3.logarithm of 81 for base 9 = log9(81) = 2,
Explanation: 92 = 81 Since 9 needs to be raised to a power of 2 to give 81, Thus logarithm of 81 base 9 is 2.
Note: An exponential function is the exact opposite of a logarithmic function. When a value is being multiplied repeatedly it is said to grow exponentially whereas when the value is being divided repeatedly it is said to grow logarithmically.
What is Logarithmic Time Complexity? A Complete Tutorial
Logarithmic time complexity is denoted as O(log n). It is a measure of how the runtime of an algorithm scales as the input size increases. In this comprehensive tutorial. In this article, we will look in-depth into the Logarithmic Complexity. We will also do various comparisons between different logarithmic complexities, when and where such logarithmic complexities are used, several examples of logarithmic complexities, and much more. So let’s get started.
What is Logarithmic Time Complexity
Table of Content
- What is a Logarithm?
- What is Complexity Analysis?
- What is Space Complexity?
- What is Time Complexity?
- How to measure complexities?
- What is a Logarithm?
- Different Types of Logarithmic Complexities
- Simple Log Complexity (Log a)
- Double Logarithm (log log N)
- N logarithm N (N * log N)
- logarithm^2 N (log^2 N)
- N^2 logarithm N (N^2 * log N)
- N^3 logarithm N (N^3 log N)
- logarithm √N (log √N)
- Examples To Demonstrate Logarithmic Time Complexity
- Practice Problems for Logarithmic Time Complexity
- Comparison of various Logarithmic Time Complexities
- Frequently Asked Questions(FAQ’s) on Logarithmic Time Complexity
- Conclusion
Contact Us