Disadvantages of the Greedy Approach
- Not always optimal: Greedy algorithms prioritize local optima over global optima, leading to suboptimal solutions in some cases.
- Difficult to prove optimality: Proving the optimality of a greedy algorithm can be challenging, requiring careful analysis.
- Sensitive to input order: The order of input data can affect the solution generated by a greedy algorithm.
- Limited applicability: Greedy algorithms are not suitable for all problems and may not be applicable to problems with complex constraints.
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