In mathematics, computer science, and economics, dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. It is applicable to problems exhibiting the properties of overlapping subproblems which are only slightly smaller and optimal substructure (described below). When applicable, the method takes far less time than naive methods.
The key idea behind dynamic programming is quite simple. In general, to solve a given problem, we need to solve different parts of the problem (subproblems), then combine the solutions of the subproblems to reach an overall solution. Often, many of these subproblems are really the same. The dynamic programming approach seeks to solve each subproblem only once, thus reducing the number of computations: once the solution to a given subproblem has been computed, it is stored or "memo-ized": the next time the same solution is needed, it is simply looked up. This approach is especially useful when the number of repeating subproblems grows exponentially as a function of the size of the input.
Read more about Dynamic Programming: History, Overview, Algorithms That Use Dynamic Programming
Famous quotes containing the words dynamic and/or programming:
“We Americans have the chance to become someday a nation in which all radical stocks and classes can exist in their own selfhoods, but meet on a basis of respect and equality and live together, socially, economically, and politically. We can become a dynamic equilibrium, a harmony of many different elements, in which the whole will be greater than all its parts and greater than any society the world has seen before. It can still happen.”
—Shirley Chisholm (b. 1924)
“If there is a price to pay for the privilege of spending the early years of child rearing in the driver’s seat, it is our reluctance, our inability, to tolerate being demoted to the backseat. Spurred by our success in programming our children during the preschool years, we may find it difficult to forgo in later states the level of control that once afforded us so much satisfaction.”
—Melinda M. Marshall (20th century)