Dynamic programming is a powerful problem-solving technique used in computer science to tackle complex problems by breaking them down into simpler subproblems. It involves solving each subproblem just once and storing the solutions to avoid redundant calculations. This approach is particularly effective for optimization problems, where the goal is to find the best solution among many possible options. The main idea behind dynamic programming is to use a technique called memoization, which involves storing previously computed results in a table (or array) for later retrieval. This significantly reduces the time complexity of recursive algorithms, turning exponential time solutions into polynomial time solutions. Dynamic programming is widely used in various applications, such as finding the shortest path in graphs (e.g., Floyd-Warshall algorithm), solving the knapsack problem, calculating Fibonacci numbers efficiently, and optimizing resource allocation in scheduling problems. For example, in the knapsack problem, dynamic programming helps to determine the most valuable combination of items to include in a knapsack without exceeding its weight limit. Understanding dynamic programming is crucial for mastering data structures and algorithms, as it provides a framework for developing efficient solutions to a wide range of computational problems. Additionally, dynamic programming is often a topic of interest in technical interviews, making it essential for developers to grasp its concepts and applications thoroughly.