Greedy Algorithms
Greedy Algorithms in DSA
Learn how greedy strategies solve optimization problems efficiently.
📌 Topics Covered
🚀 Introduction
Greedy algorithms make the best choice at each step to solve optimization problems efficiently.
💡 Core Idea: Choose the locally best option → hope for global optimum.
How It Works
- Pick best option at current step
- Never revisit previous decisions
- Works when problem has optimal substructure
🧠 Key Concepts
Greedy Choice Property
Local optimal choices lead to global optimum.
Optimal Substructure
Problem can be broken into smaller optimal subproblems.
Limitation
Does NOT always give correct answer (unlike DP).
🧪 QA & Common Mistakes
Common Mistakes
- Wrong greedy choice
- Ignoring constraints
- Not proving correctness
QA Techniques
- Test edge cases
- Compare with brute force
- Analyze time complexity
📚 Important Problems
- Minimum Spanning Tree
- Knapsack (Fractional)
- Dijkstra’s Algorithm
- Activity Selection
🌍 Real-world Applications
- Task scheduling
- Network routing
- Data compression (Huffman Coding)
🎯 Conclusion
Greedy algorithms are simple and fast, but require careful validation to ensure correctness.