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Introduction to Data Structures & Algorithms in Java

The Big O notation

From the course: Introduction to Data Structures & Algorithms in Java

  • Course details

    Enhance your programming skill set by learning about some of the most commonly-used data structures and algorithms. In this course, instructor Raghavendra Dixit walks through how to use Java to write code to implement data structures and algorithms. After explaining why it's advantageous to study these topics, he goes over the analysis of algorithms and discusses arrays—a data structure found in most programming languages. He also explains how to implement linked lists in Java, and covers stacks, queues, recursion, binary search trees, heaps, and more.

    Note: This course was created by Packt Publishing. We are pleased to host this training in our library.

    Instructor

    • Click here to view Raghavendra Dixit’s instructor page

      Raghavendra Dixit

      • Raghavendra Dixit is an entrepreneur and technical architect with over 15 years of programming experience.

        A graduate of the Indian Institute of Technology, Raghavendra has worked in both product and service-based software companies. He has worked with languages such as Perl, Java, Objective-C, Scala, and JavaScript. He has also used various frameworks and platforms, including Spring, Play, Cocoa, and Android. In his free time, he likes to create technical content that's easy to follow, and helps people in the software industry enhance their skills and accelerate their careers.

    Skills covered in this course

  • Introduction

    - [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…

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Contents