Book Image

Introduction to Algorithms

By : Cuantum Technologies LLC
Book Image

Introduction to Algorithms

By: Cuantum Technologies LLC

Overview of this book

Begin your journey into the fascinating world of algorithms with this comprehensive course. Starting with an introduction to the basics, you will learn about pseudocode and flowcharts, the fundamental tools for representing algorithms. As you progress, you'll delve into the efficiency of algorithms, understanding how to evaluate and optimize them for better performance. The course will also cover various basic algorithm types, providing a solid foundation for further exploration. You will explore specific categories of algorithms, including search and sort algorithms, which are crucial for managing and retrieving data efficiently. You will also learn about graph algorithms, which are essential for solving problems related to networks and relationships. Additionally, the course will introduce you to the data structures commonly used in algorithms. Towards the end, the focus shifts to algorithm design techniques and their real-world applications. You will discover various strategies for creating efficient and effective algorithms and see how these techniques are applied in real-world scenarios. By the end of the course, you will have a thorough understanding of algorithmic principles and be equipped with the skills to apply them in your technical career.
Table of Contents (14 chapters)
11
Conclusion
12
Where to continue?
13
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4.3 Dynamic Programming Algorithms

Dynamic Programming (DP) is an algorithmic technique that breaks down an optimization problem into simpler subproblems. The solution to the overall problem depends on the optimal solution to its subproblems.

The advantage of DP over divide-and-conquer algorithms is that DP is applicable to dependent subproblems. In other words, when subproblems share sub-subproblems, DP will work while divide-and-conquer algorithms will not.

DP algorithms solve subproblems just once and then save the answer in a table. This avoids the need to re-compute the answer every time the subproblem is encountered. This saves time and frees up computing power to solve other problems.

DP is a powerful technique that has many applications in areas such as computer science, economics, and engineering. By breaking down complex problems into smaller, more manageable subproblems, DP enables us to solve problems that would otherwise be too difficult to tackle.

Consider the problem...