Which of the following describes dynamic programming?

Dynamic programming is a technique used to solve complex problems by breaking them down into simpler subproblems, which can be solved more easily and then combined to form a solution to the original problem. This approach is particularly useful for optimization problems where the solution can be built from previously computed results.

In dynamic programming, each subproblem is solved and stored so that the solution can be reused, which is more efficient than recomputing solutions for the same subproblems multiple times. This characteristic makes it particularly effective for problems with overlapping subproblems and optimal substructure.

The other options do not accurately reflect the essence of dynamic programming. For instance, sorting data is not the central purpose of dynamic programming; instead, it's focused on problem-solving and optimization. Additionally, while recursive calls can be part of some dynamic programming solutions, they usually involve data storage (such as memoization) to avoid redundant calculations. Finally, directly computing all possible data combinations is more aligned with brute-force methods rather than the strategic optimization approach that dynamic programming employs.