How does dynamic programming differ from static programming?

Dynamic programming is characterized by its ability to adapt based on runtime data, making it especially powerful for solving complex problems that can be broken down into overlapping subproblems. This adaptability allows the algorithm to use previously computed results at runtime, optimizing the process by avoiding redundant calculations. In contexts where decision-making can change based on input received during the execution, dynamic programming is able to adjust accordingly, making it inherently flexible and efficient.

This adaptability is crucial in scenarios where the state of computation can change dynamically depending on user input or other variables, allowing for more responsive and efficient algorithms. By storing and reusing values that have already been computed, dynamic programming ensures that the same subproblem is not solved multiple times, ultimately leading to significant performance improvements in terms of time complexity.

Other approaches, such as static programming, may not have the same capacity to adjust to runtime conditions or to take advantage of previously computed data in a dynamic manner, thus limiting their flexibility compared to dynamic programming.