The term “dynamic programming” describes a method that is used in the field of mathematical optimization , as well as in computer programming. In simple terms, it is an optimization technique that helps solve a class of problems by dividing them into smaller problems. Marketplace development services may also use this technology in various ways.
This method may sound quite difficult at first, especially if you struggle with algorithmic interviews in web programming companies, but it mainly refers to solving a complex problem in a more simple way, by separating it into a sequence of decision steps. The main idea of dynamic programming is that the overall problem can be solved using solutions to its subproblems that are created in the process of division. Two ways of dealing with problems are usually applied.
Besides that, dynamic programming is also applied in areas like economics, as well as aerospace engineering. Mobile and web development companies use this technique for algorithmic interviews. In short, dynamic programming is crucial in writing algorithms, and custom web application development companies apply this technology as well.
Understanding Dynamic Programming
To put it simply, dynamic programming helps solve different problems in a unique way. This optimization technique allows you to divide problems and form subproblems, or small versions of problems. In a top-down way in dynamic programming, if there are overlapping subproblems, the solutions for them can be saved so in the future we will not have to calculate the result again.
The opposite method is also utilized, in which you solve the subproblems or minor problems, and then you solve the major problem itself. The method is called the bottom-up method.
How the Method Works
Dynamic programming offers two main ways that help you solve problems. They are:
- The top-down method with memoization;
- The bottom-up with tabulation.
These processes are, in general, opposites of each other. Tabulation is the process that avoids recursion and the problem is solved by solving the subproblems first.
In the top-down method with memoization, after dividing the problem into subproblems, you need to find the optimal solution. In case of overlapping subproblems, the solution is saved and the process is called memoization. Memoization helps solve subproblems in the future. The value of subproblems is repeatedly calculated.
So, as we can see from the article, dynamic programming is a method of solving complex problems. But unlike greedy algorithms, dynamic programming is a technique that is not looking for a greedy choice. Also, a greedy choice does not guarantee a global optimum, so, generally speaking, greedy algorithms may only make a guess and this is a risky decision in the long run. Dynamic programming, on the other hand, finds optimum solutions by combining the results made by solving the subproblems.
Although it is a pretty tricky method, dynamic programming is a really efficient and useful technique. In order to be skilled at it, you need a lot of time, but the result will be worth it. The efficiency it provides stands out among other methods that deal with algorithms.