Grokking Dynamic Programming Interview in JavaScript

Dynamic programming is more efficient because it eliminates redundant computations by storing the results of subproblems. In brute-force methods, the same subproblems are often recalculated multiple times, leading to exponential time complexity. DP reduces this by solving each subproblem once and using that result whenever needed, cutting the overall time complexity.

The choice between memoization and tabulation depends on the problem and the desired approach. If you’re more comfortable with recursion and want to solve the problem top-down, memoization is ideal. Tabulation, however, is preferred when you can iteratively build the solution from base cases up in a bottom-up manner, and it’s often easier to visualize and manage certain problems.

One common mistake is not correctly identifying overlapping subproblems or optimal substructure, leading to inefficient solutions. Another issue is poor table or memoization setup, such as failing to initialize base cases correctly. Also, forgetting to handle edge cases or ignoring time and space complexity optimizations can lead to suboptimal performance.

Dynamic programming is used in various real-world applications, such as optimization problems in finance (e.g., portfolio optimization), operations research (e.g., supply chain management), and computer science (e.g., text processing, network routing). It’s particularly useful in problems where decisions must be made sequentially, and resource optimization is critical.