Dynamic programming with memoization stores results of subproblems to avoid redundant calculations.

Dynamic programming (DP) with memoization is an optimization technique that solves problems by breaking them down into overlapping subproblems and storing the results of each subproblem so that they can be reused. This reduces the time complexity of recursive solutions by avoiding redundant calculations. Memoization is typically implemented using a hash table or array to store the results of function calls. This technique is especially useful in problems like the Fibonacci sequence, where the same subproblems are solved multiple times. By using DP with memoization, you can optimize recursive algorithms and solve problems efficiently.