Both Greedy and DP solve optimization problems, but they differ fundamentally:
Greedy:
- Makes the locally optimal choice at each step
- Fast: usually O(n log n) or O(n)
- Does NOT always find the global optimum
- Works when the "greedy choice property" holds
Dynamic Programming:
- Considers ALL possible choices
- Finds the globally optimal solution — always
- Slower: usually O(n × m) time and space
- Works for problems with overlapping subproblems
Example — Coin Change with coins [1, 4, 6], amount 8:
Greedy picks 6+1+1 = 3 coins (suboptimal!)
DP finds 4+4 = 2 coins (optimal!)
Related algorithms
Frequently asked questions
- What is Greedy vs Dynamic Programming?
- Both Greedy and DP solve optimization problems, but they differ fundamentally:
- What is the complexity of Greedy vs Dynamic Programming?
- Greedy vs Dynamic Programming is explained with a step-by-step visualization, including time and space complexity where applicable.
- Who is this Greedy vs Dynamic Programming visualizer for?
- The Greedy vs Dynamic Programming visualization targets advanced-level learners in the Concepts category. Useful for students, interview prep, and hands-on review.
- What algorithms are related to Greedy vs Dynamic Programming?
- In the same category (Concepts) you can explore: Big O Notation, Recursion, Two Pointers. Each has an interactive visualization.