Greedy vs Dynamic Programming: Complete Comparison for Interviews
Choosing between Greedy and Dynamic Programming is a common dilemma in coding interviews. Both are powerful algorithmic patterns, but they solve different types of problems. This guide provides a head-to-head comparison with feature matrix, use-case scenarios, and a clear verdict on when to use each.
Feature Comparison Matrix
What is Greedy?
Make locally optimal choices for globally optimal solutions.
Best for: Activity selection, Job scheduling, Huffman coding
Time Complexity: O(n log n)
When to use: Choose Greedy when the problem involves Activity selection or Job scheduling.
What is Dynamic Programming?
Optimize recursive solutions with memoization and tabulation.
Best for: Optimization problems, Counting problems, Decision problems
Time Complexity: O(n²) typical
When to use: Choose Dynamic Programming when the problem involves Optimization problems or Counting problems.
Key Differences
Use-Case Recommendations
Verdict
Use Greedy when: Activity selection, Job scheduling, Huffman coding.
Use Dynamic Programming when: Optimization problems, Counting problems, Decision problems.
Both combined: In some problems, you can use Greedy as a sub-routine within Dynamic Programming or vice versa. Look for these hybrid opportunities in Hard-level problems.
Both patterns are essential for a well-rounded interview preparation. Master each individually before attempting to combine them.
Frequently Asked Questions
Is Greedy harder than Dynamic Programming?
Can I use Greedy and Dynamic Programming together?
Which should I learn first?
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