This repository contains the materials created for the course "Analysis and Algorithm Techniques". The objective of this course is to provide students with the fundamentals of algorithm analysis, design, and implementation. The course covers different algorithmic paradigms, including divide-and-conquer, dynamic programming, and greedy algorithms. The materials in this repository are intended to be used as a reference for the topics covered in the course.
The following topics are covered in this repository:
This section covers the basic concepts of algorithm correctness and how to prove it. You will find examples of algorithms with their proofs of correctness.
This section covers the basics of algorithm analysis, including time complexity, space complexity, and Big O notation. You will find examples of algorithms with their analysis.
This section covers the divide-and-conquer paradigm, which involves breaking down a problem into smaller sub-problems that are more manageable to solve. You will find an example of the Closest Pair Problem, which is a classic divide-and-conquer problem.
This section covers linear time sorting algorithms, including Count Sort and Radix Sort. You will find examples of these algorithms with their implementation.
This section covers the greedy algorithm paradigm, which involves making locally optimal choices at each step with the hope of finding a global optimum. You will find examples of greedy algorithms with their implementation.
This section covers dynamic programming, which is a technique for solving complex problems by breaking them down into simpler subproblems. You will find examples of dynamic programming with their implementation.
This repository serves as a reference for the topics covered in the course "Analysis and Algorithm Techniques". You can use the examples provided to deepen your understanding of the topics and implement them in your own projects.
- Rafael Guimarães - Computer Engineering at IFPB