Greedy Algorithms for Computer Science and Exam Preparation

Solving complex optimization problems efficiently is a cornerstone of computer science and a critical skill for passing academic and competitive exams. This course provides a clear pathway to understanding how and when to apply the greedy design paradigm. You will transition from basic algorithmic definitions to confidently analyzing and dry-running classic optimization problems. What you'll learn: - Understand the foundational properties of greedy choice and optimal substructure. - Solve the fractional knapsack problem using step-by-step mathematical formulations. - Construct minimum spanning trees using Prim's and Kruskal's algorithms. - Find shortest paths in weighted graphs using Dijkstra's algorithm. - Analyze time and space complexity using Big O notation for each greedy approach. - Practice tracing algorithms manually with structured written exercises designed for exam preparation. We begin with essential terminology and the core theory of optimization. From there, you will explore classic greedy problems, learning how to prove correctness, analyze complexity, and implement the logic step-by-step. This text-only course is designed for computer science students, exam candidates, and programmers looking to strengthen their theoretical foundations. No advanced prerequisites are required. Begin reading today to sharpen your algorithmic problem-solving skills.