Greedy Algorithm Vs Dynamic Programming
Below are the comparison of Greedy Algorithm and Dynamic Programming based on various criteria:
Criteria | Greedy Algorithm | Dynamic Programming |
---|---|---|
Basic Idea | Makes the locally optimal choice at each stage | Solves subproblems and builds up to the optimal solution |
Optimal Solution | Not always guaranteed to provide the globally optimal solution | Guarantees the globally optimal solution |
Time Complexity | Typically faster; often linear or polynomial time | Usually slower due to solving overlapping subproblems |
Space Complexity | Requires less memory; often constant or linear space | Requires more memory due to storing intermediate results |
Subproblems Overlapping | Does not handle overlapping subproblems | Handles overlapping subproblems efficiently |
Examples | Finding minimum spanning tree, Huffman coding | Matrix chain multiplication, shortest path problems |
Applications | Used when a greedy choice at each step leads to the globally optimal solution | Applied when the problem can be broken down into overlapping subproblems |
Greedy Algorithm Tutorial – Examples, Application and Practice Problem
Greedy Algorithm is defined as a method for solving optimization problems by taking decisions that result in the most evident and immediate benefit irrespective of the final outcome. It works for cases where minimization or maximization leads to the required solution.
Table of Content
- What is Greedy Algorithm?
- Characteristics of Greedy Algorithm
- Examples of Greedy Algorithm
- Why to use Greedy Approach?
- How does the Greedy Algorithm works?
- Greedy Algorithm Vs Dynamic Programming
- Applications of Greedy Algorithms
- Advantages of Greedy Algorithms
- Disadvantages of the Greedy Approach
- Greedy Algorithm Most Asked Interview Problems
- Frequently Asked Questions on Greedy Algorithm
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