The course explores algorithmic paradigms and methods that enable the development of efficient polynomial-time algorithms. Key paradigms discussed include greedy algorithms, divide and conquer, dynamic programming, and local search. These paradigms are used to develop efficient exact and approximation algorithms for a variety of problems motivated by real-life applications. Additionally, the course covers basic computational complexity concepts that establish theoretical limits on what can be solved efficiently.

By the end of this course, students should be able to:
- Design and implement efficient algorithms using key paradigms such as greedy algorithms, divide and conquer, dynamic programming, and local search.
- Develop both exact and approximation algorithms to solve real-world problems efficiently.
- Analyze the performance of algorithms and understand the trade-offs between optimality and computational feasibility.
- Apply basic concepts of computational complexity to determine the theoretical limits of efficient algorithms.