I’m having trouble understanding how dynamic programming improves upon simple recursion. I know both techniques involve breaking a problem into subproblems, but how exactly does dynamic programming make solving these subproblems more efficient? Can someone clarify the concept of memoization and how it prevents redundant calculations in dynamic programming?

I studied examples of recursive algorithms and looked at dynamic programming concepts, but the improvement dynamic programming offers over simple recursion wasn’t clear. I was expecting to understand how dynamic programming avoids redundant calculations and achieves efficiency through techniques like memoization or tabulation.