Dynamic programming is a method for solving problems in which the solution depends on information from more than one previous problem. It is used when sub-problems are repeated repeatedly, but each time with different data.
Dynamic programming algorithms are usually faster than their brute force counterparts because they avoid repeating work that has already been done. They can also be applied to optimization problems where finding the best answer requires looking at every possible answer, not just the first one or two, as in many other algorithms.
It's an algorithm that uses the results of previously solved sub-problems to speed up the solution process. This type of algorithm is mostly used for optimization, but it can also be applied to other types of problems. Before solving an in-hand sub-problem, dynamic algorithms will try to examine the results of previous sub-problems and use them if they are available.
The algorithm will look at all of your options before making any decisions, so it's great for puzzles where there may only be one correct answer (or very few). This makes dynamic programming useful for scheduling tasks and planning routes around cities. You could even use it to plan out how much money you should spend on Christmas presents this year if you want.
For example, the shortest path problem asks for the least expensive way of driving from one city to another through certain cities only once (and returning). The traveling salesman problem asks which route should be taken to minimize the traveled distance. These are both examples of NP-hard problems.
In dynamic programming, we solve such hard optimization problems by breaking them down into smaller subproblems and storing their solutions in an array or table indexed by the solution of larger instances of these subproblems.
Dynamic Programming is a powerful tool that allows us to solve many difficult computational tasks easily. Visit our blog section to learn more about programming techniques.