Greedy Vs Dynamic Programming

Greedy Vs Dynamic Programming: Which One Is Better For You In 2023?

Looking for concepts of Greedy Vs Dynamic Programming? Are you curious to know these terms “greedy programming” and “dynamic programming”? They are two important techniques for solving complex problems. Greedy programming involves making the best choice at each step, hoping it leads to the best overall solution. 

On the other hand, dynamic programming breaks a big problem into smaller subproblems, solving them and combining their solutions to solve the larger problem. While greedy programming focuses on immediate gains, dynamic programming considers the entire problem. Greedy programming is simpler and faster, but it may not always find the optimal solution. Dynamic programming guarantees the optimal solution, but it can be slower.

In this blog we will cover the meaning of both terms and their characteristics. In addition their similarities and differences. We will also have a look on the comparison of both terms on the basis of google trends. By comparing their popularity on Google Trends, we can see which one is more commonly searched for. Each technique is useful for solving different types of problems, and we will explore some examples in this blog.

Stay tuned with us to know the various concepts of greedy vs dynamic programming.

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What Is Greedy Programming? 

Greedy programming, also known as the greedy algorithm, is a problem-solving approach used in computer programming. It involves making choices that seem to be the best at each step without worrying too much about what might happen later. 

The idea is to pick the option that looks the most appropriate at the moment. By doing this repeatedly, we can quickly find a solution that is pretty good, although it may not be the absolute best. 

However, it is important to remember that the greedy approach does not always give the best result. Sometimes, it can lead to a solution that is not as good as it could be or even completely wrong. So, it is important to think carefully and decide if using the greedy strategy is the right choice for a particular problem. 

Characteristics Of Greedy Programming

  • Locally optimal choices are made at each step.
  • Focus on immediate optimization without considering long-term consequences.
  • Greedy selection of options with the highest perceived benefit.
  • Decisions made are final with no backtracking.
  • Greedy algorithms may not always yield the globally optimal solution.

What Is Dynamic Programming?

Dynamic programming is a method used in computer programming to solve problems by breaking them up into smaller, easier-to-handle parts. It involves solving each subproblem only once and storing the results, so they can be reused whenever needed. 

This method works best when a problem can be broken up into smaller problems that come together. By solving and storing the solutions to these subproblems, dynamic programming reduces redundant computations and improves efficiency.

To apply dynamic programming, we typically start by solving the smallest subproblems first and then gradually build up to larger subproblems, utilizing the solutions we have already computed. 

Characteristics Of Dynamic Programming

  • Breaks down complex problems into smaller subproblems.
  • Each subproblem is resolved once and the results are stored.
  • Utilizes memoization to reuse stored solutions.
  • Reduces redundant computations.
  • Effective for problems with overlapping subproblems.
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What Is The Differences Between Greedy Vs Dynamic Programming? 

Here are some differences between greedy vs dynamic programming : 

Basis Greedy ProgrammingDynamic Programming
ApproachMakes locally best choices at each step.Breaks down problems into smaller parts and solves them step by step.
OptimizationFocuses on immediate gains without considering long-term. consequencesAims for the best overall solution by solving subproblems and combining their solutions.
BacktrackingDecisions made at each step are final and cannot be changed.Allows revisiting and revising solutions if better alternatives are found.
EfficiencyQuick and simple, but may not always yield the best result.Slower due to solving subproblems, but guarantees the best possible solution.
ApplicationSuitable for simple problems where greedy choices lead to optimal solution.Effective for complex problems with overlapping subproblems.

What Is The Similarities Between Greedy Vs Dynamic Programming? 

Here are some similarities between greedy vs dynamic programming : 

1. Problem Decomposition

Both greedy and dynamic programming involve breaking down complex problems into smaller, more manageable subproblems. This helps in approaching the problem in a structured manner.

2. Optimal Solutions

Both approaches aim to find the best possible solution for the given problem. They strive for optimization, whether it is maximizing or minimizing a certain objective.

3. Overlapping Subproblems

Both greedy programming and dynamic programming can be effective for problems that have overlapping subproblems. This means that the solutions to smaller subproblems can be reused, reducing redundancy and improving efficiency.

4. Algorithmic Techniques

Both approaches are problem-solving techniques commonly used in computer programming. They provide structured methods for solving problems and are applicable in various domains.

5. Independence Of Subproblems

Both approaches assume that the solution to each subproblem is independent of the solutions to other subproblems. This allows for a modular approach where individual subproblems can be solved without considering the entire problem at once.

A Comparison Of Popularity based on Google Trends: Greedy Vs Dynamic Programming

According to Google Trends, the popularity of greedy algorithms has been on a steady decline over the past five years, while the popularity of dynamic programming has been on the rise. Red colour line shows the trend of dynamic programming, while the blue shows greedy programming. In 2018, greedy algorithms were more popular than dynamic programming, but by 2023, dynamic programming was more popular.

There are a few explanations of this trend. One possibility is that greedy algorithms are simply less efficient than dynamic programming. Greedy algorithms make decisions based on local information, while dynamic programming considers all possible solutions. This can make dynamic programming more time-consuming to compute, but it can also lead to better solutions.

Another possibility is that greedy algorithms are simply less versatile than dynamic programming. Greedy algorithms can only be used to solve a limited number of problems, while dynamic programming can be used to solve a wider range of problems. This makes dynamic programming more useful for general-purpose programming.

Finally, it is also possible that the trend is simply due to changes in the way that people are learning about computer science. In the past, greedy algorithms were often taught in introductory computer science courses, while dynamic programming was taught in more advanced courses. 

However, in recent years, there has been a trend towards teaching dynamic programming in introductory courses. This may be due to the fact that dynamic programming is more efficient and versatile than greedy algorithms.

Due to the reason for the trend, it is clear that dynamic programming is becoming more popular than greedy algorithms. 

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Problems That Should Be Solved By Using Greedy Vs Dynamic Programming Algorithms 

Here are some examples of problems that should be solved using greedy vs dynamic programming algorithms:

I. Greedy Programming

Here are some examples of problems that should be solved using greedy algorithms:

1. Shortest Path Problem: Given a graph and two vertices, find the shortest path between them.

2. Maximum Value Problem : Given a set of numbers, find the maximum value.

3. Huffman coding: Given a set of symbols and their frequencies, find a code for each symbol that minimizes the average code length.

II. Dynamic Programming

Here are some examples of problems that should be solved using dynamic programming:

1. Knapsack Problem: Given a set of items with weights and values and a knapsack with a limited capacity, find the subset of items that has the maximum value and fits in the knapsack.

2. Longest Common Subsequence Problem: Given two strings, find the longest substring that appears in both strings.

3. Edit Distance Problem: Given two strings, find the minimum number of changes needed to transform one string into the other.

In general, greedy algorithms should be used for problems where speed is more important than finding the optimal solution. Dynamic programming should be used for problems where finding the optimal solution is more important than speed.

Is Greedy Algorithm Better Than Dynamic Programming?

Determining whether the greedy algorithm is better than dynamic programming is not a straightforward comparison as it depends on the problem being solved. 

Greedy algorithms are generally simpler and faster since they make locally optimal choices at each step. However, their focus on instant optimization can result in solutions that are not the best overall. 

On the other hand, dynamic programming ensures the optimal solution by breaking down the problem into smaller subproblems and reusing their solutions. 

While dynamic programming can be slower due to solving overlapping subproblems, it guarantees the best possible outcome. 

The choice between the two approaches depends on the problem’s characteristics, time constraints, and the desired level of optimality.


We have learned about greedy programming and dynamic programming. We saw how they are different and similar in solving problems. Greedy programming looks for quick benefits, while dynamic programming aims for the best overall solution. 

By comparing their popularity on Google Trends, we found out which one is more popular. We also discovered that each technique is suitable for solving different types of problems. With this knowledge, we can approach problem-solving with a better understanding and choose the right technique for each situation.

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