Welcome to the CS2000 course repository for "Design and Analysis of Algorithms." This repository contains comprehensive notes, well-commented source codes, and various resources to support your learning journey throughout the course.
This course delves into the intricacies of algorithm design and analysis, providing you with a solid foundation in both theoretical concepts and practical implementations. By the end of the course, you will have gained a deep understanding of various algorithmic techniques and their applications in solving complex computational problems.
In this section, you'll learn the basics of measuring algorithm efficiency, including time complexity, space complexity, and Big O notation. Understanding how to analyze algorithms' performance is crucial for designing efficient solutions.
Explore asymptotic notations like Big O, Big Theta, and Big Omega. Dive into the mathematical analysis of algorithms, allowing you to express their efficiency in precise terms and compare different algorithms objectively.
Learn the divide and conquer strategy, where complex problems are broken down into smaller subproblems, solved recursively, and then combined to obtain the final solution.
Discover the decrease and conquer technique, where you iteratively reduce a problem's size until it becomes trivial to solve.
Explore the transform and conquer approach, which involves transforming a problem into a different form that is easier to solve, and then solving it using appropriate techniques.
Study greedy programming, where you make locally optimal choices at each step to eventually reach a globally optimal solution.
Dive into dynamic programming, a powerful technique for solving problems by breaking them into overlapping subproblems and storing their solutions to avoid redundant computations.
Understand the limitations of what algorithms can achieve. Explore problems that are inherently difficult or impossible to solve using traditional algorithms.
Get a glimpse into the cutting-edge world of quantum computing and how it has the potential to revolutionize the way we solve complex computational problems.
Upon completing this course, you will have achieved the following learning outcomes:
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Design Efficient Algorithms: You will be able to formulate algorithmic solutions for various computational problems and determine their efficiency through rigorous mathematical analysis.
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Apply Diverse Algorithmic Techniques: You'll gain hands-on experience applying different algorithmic strategies, including brute-force, divide and conquer, decrease and conquer, greedy programming, dynamic programming, and transform and conquer.
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Problem Classification: You'll be able to classify problems into different complexity classes, distinguishing between problems that can be solved efficiently and those that are inherently challenging.
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Quantum Computing Understanding: Gain a foundational understanding of quantum computing and its potential to tackle problems that are beyond the capabilities of classical computers.
School of Computer Science & Engineering
B.Tech(H) Program
Semester 3
