Memoization is an optimization technique used to speed up recursive algorithms by storing the results of expensive function calls and reusing them when the same inputs occur again. Instead of recomputing overlapping subproblems, memoization caches the answer and returns it instantly when needed. This approach is a core concept behind many Dynamic Programming solutions.

Memoization is most commonly applied to problems that naturally use Recursion. When a recursive function repeatedly solves the same subproblem, storing results in a cache—often implemented with a Hash Table or array—can dramatically reduce time complexity.

In coding interviews, memoization helps transform inefficient exponential solutions into efficient polynomial-time algorithms. You'll frequently see it used in problems involving sequences, paths, or decision trees.

• Optimizing recursive algorithms with repeated subproblems
• Caching computed results to avoid redundant work
• Solving top-down dynamic programming problems
• Improving performance in combinatorial and path-counting problems

Mastering memoization allows you to quickly recognize overlapping subproblems and implement efficient solutions during interviews. Practicing these problems will also strengthen your intuition for transitioning from recursion to dynamic programming.