The purpose of memoization in dynamic programming is to optimize the computation time of a recursive algorithm. Dynamic programming involves breaking down a problem into smaller subproblems, solving each subproblem only once, and storing the solution for future use.

  Memoization is a technique where the computed results of expensive function calls are stored and reused instead of recomputing them again. It involves using a data structure such as a memoization table or cache to store previously computed solutions.

  By memoizing the results, the algorithm can avoid redundant computations by looking up the previously computed solutions in the memoization table. This greatly reduces the overall computation time and improves the efficiency of the algorithm.

  In dynamic programming, subproblems are often solved recursively. Without memoization, the same subproblems would be solved multiple times, leading to inefficiency. By storing the results of these subproblems in the memoization table, the algorithm can retrieve them instantly when needed, saving computation time.

  Overall, memoization plays a crucial role in dynamic programming by eliminating redundant computations and improving the efficiency of the algorithm. It helps reduce the time complexity and allows dynamic programming algorithms to solve larger and more complex problems in a reasonable amount of time.