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’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’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?