In the world of programming, there are two main approaches to solving problems; greedy and dynamic programming. Greedy programming is the approach that tries to solve a problem as quickly as possible, while dynamic programming is the approach that tries to solve a problem as efficiently as possible.

In greedy programming, you try to solve a problem as quickly as possible by trying to find the smallest possible solution. In dynamic programming, you try to solve a problem as efficiently as possible by trying to find the smallest possible solution. Both approaches have their advantages and disadvantages. Greedy programming can be very time-consuming if you are trying to solve a very large problem, but it can also be very efficient if you are trying to solve a small problem. On the other hand, dynamic programming can be very time-consuming if you are trying to solve a very small problem, but it can also be very efficient if you are trying to solve a large problem.

As a general rule, dynamic programming is better than greedy programming when you are solving problems that involve large numbers of variables or when you are solving problems that involve a lot of data. On the other hand, greedy programming is better than dynamic programming when you are solving problems that involve small numbers of variables or when you are solving problems that involve a lot of data. A dynamic programming algorithm is a powerful tool that can be used to solve problems more efficiently than with other algorithms.

The greedy approach is used to answer problems involving optimization. It is a strategy that focuses on obtaining the maximum or minimum result. We can better understand the term problem with a few terms. The simplest and most straightforward method is the Greedy method. It is not an algorithm but a technique. The decision is made on the basis of current information only, regardless of future impact. This method determines whether or not an optimal solution is possible. 

Whenever we encounter a recursive form that has identical inputs for repeated uses, we can lower time complexity using dynamic programming. We may save the results of subproblems by recording them. This minimal optimization reduces time complexity to a polynomial ratio. For example, if we recorded all the answers to the Fibonacci summation, time complexity would be reduced to linear. If we recorded subproblems, time complexity would be reduced to linear. 

There are several key differences between the greedy method and dynamic programming, Check on Interviewbit.