Word count: 3500 words
Objectives to cover:
Introduction to Algorithm Analysis – Understanding the fundamentals of evaluating algorithm efficiency and correctness.
Asymptotic Notations – Exploring Big O, Big Theta, and Big Omega to describe algorithm growth rates.
Data Structures and Their Impact on Algorithm Performance – Analyzing how choice of data structures influences time and space complexity.
Sorting and Searching Algorithms – Reviewing efficient techniques and best practices for organizing and locating data.
Graph Algorithms – Studying traversal methods, shortest path solutions, and minimum spanning tree algorithms.
NP-Complete and NP-Hard Problems – Defining computationally intractable problems and exploring classic examples.
Approximation Algorithms and Heuristics – Employing near-optimal strategies for solving hard problems efficiently.
Parallel and Distributed Algorithms – Leveraging concurrency to enhance performance and scalability of algorithms.
Challenges and Future Directions in Algorithm Design – Addressing open problems and emerging trends in algorithmic research.
Reference: IEEE style