LeetCode 150 Roadmap - 2D Dynamic Programming Part 1 (Longest Common Subsequence, Coin Change II)

Two-dimensional dynamic programming (2D DP) is a powerful technique used to solve optimization problems where the solution can be broken down into simpler, overlapping subproblems that can be stored and reused. It involves creating a 2D table (or matrix) where each cell represents a state of the problem, typically defined by two variables. The values in these cells are computed iteratively based on the relationships or transitions between states, often derived from the problem's constraints and requirements. This approach is commonly used in problems involving sequences, such as the longest common subsequence, edit distance, and knapsack problems.

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