For dynamic programming, focus on breaking problems into smaller subproblems and using memoization to avoid redundant calculations. Practice common DP problems like the knapsack problem or longest increasing subsequence.

Dynamic programming (DP) is one of the trickiest yet most rewarding techniques in competitive programming. The core idea of DP is to break a complex problem into smaller subproblems and store the results of these subproblems to avoid redundant calculations (a process known as memoization). To approach a DP problem, start by identifying if it exhibits overlapping subproblems and optimal substructure. If so, try to formulate the problem in terms of states, where each state represents a subproblem. Then, determine the transition between states (i.e., how one state leads to another). Once you've done that, you can implement the solution either top-down (using recursion and memoization) or bottom-up (using iteration and tabulation). Common examples of DP problems include the knapsack problem, the longest common subsequence, and the longest increasing subsequence. In contests, you might encounter variations of these problems that require additional twists, such as pathfinding in grids or string manipulation. The best way to get better at DP is through consistent practice, starting with simple problems and gradually moving on to more complex ones. Recognizing DP problems in a contest and knowing how to approach them efficiently will significantly improve your performance.