Dynamic Programming Questions Medium
Dynamic Programming is a programming technique used to solve complex problems by breaking them down into smaller overlapping subproblems and solving each subproblem only once, storing the solution in a table or memoization array. This approach allows for efficient computation by avoiding redundant calculations.
Unlike other programming techniques, such as divide and conquer or greedy algorithms, dynamic programming focuses on solving subproblems and reusing their solutions to solve larger problems. It is particularly useful when a problem can be divided into smaller subproblems that exhibit optimal substructure, meaning the optimal solution to the larger problem can be constructed from the optimal solutions of its subproblems.
Dynamic programming also differs from other techniques in its use of memoization, which involves storing the solutions to subproblems in a table or array. This allows for quick retrieval of previously computed solutions, avoiding the need to recalculate them. By reusing these solutions, dynamic programming can significantly reduce the time complexity of a problem.
Furthermore, dynamic programming is often used for optimization problems, where the goal is to find the best solution among a set of possible solutions. It can be applied to a wide range of problems, including but not limited to, shortest path problems, sequence alignment, knapsack problems, and scheduling problems.
In summary, dynamic programming is a programming technique that breaks down complex problems into smaller subproblems, solves each subproblem only once, and stores the solutions for efficient computation. It differs from other programming techniques by its focus on subproblem optimization, use of memoization, and applicability to a wide range of problems.