Join Raghavendra Dixit for an in-depth discussion in this video The Big O notation, part of Introduction to Data Structures & Algorithms in Java.
- [Instructor] Now we come to the math of time complexity.…To understand time complexity in a form…of a very simple expression.…Computer scientist define the Big O notation,…which is one of the many other notations dealing…with time complexity.…Like the teton notation, the small notation and on.…But in this course, of course we only…deal with the bigger notation.…Let's say that the order of time that we found for…an algorithm is a quadratic function…in N as displayed here through this graph.…
And their job is to find the upper bound for this upper…function, T(n), alright?…Now consider a function C one N squared,…and here seven is so chosen that this function overtakes…the function T(n).…But then we can choose another constant C two,…such that C two N squared is always greater…than or equal to the T(n) for all values of N greater than…N not, can you see that?…Now we can say that C two N squared…is in upper bound of of T(n).…
Because of after some value of N C two N squared will…always be greater than T(n), no matter what.…
Note: This course was created by Packt Publishing. We are pleased to host this training in our library.
- Why study data structures and algorithms?
- How to calculate the time complexity
- Using Big O notation
- Using basic sorting and search algorithms
- Searching elements in unordered arrays and ordered arrays
- Implementing a linked list in Java
- Implementing stacks using arrays
- Queues using arrays
- Binary search trees
- Representing heaps using arrays
Skill Level Intermediate
1. Introduction to Algorithms
2. Analysis of Algorithms
3. Basic Sorting and Search Algorithms
4. Linked Lists
5. Stacks and Queues
7. Binary Search Trees
8. More Sorting Algorithms
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