Dynamic programming (DP) is one of the most challenging topics in competitive programming, but with practice, you can develop an intuition for solving DP problems. The key to dynamic programming is recognizing when a problem can be broken down into smaller overlapping subproblems and when the optimal solution to the overall problem can be built from the solutions to these subproblems. Many classic problems, such as the knapsack problem, the longest common subsequence, and the matrix chain multiplication, have standard DP solutions that you should learn and understand. Start by practicing these common patterns and then gradually work your way up to more complex problems. When solving a DP problem, the first step is to define the state variables and the recurrence relation that will allow you to build the solution from subproblems. You'll also need to identify the base cases, which are the simplest subproblems that can be solved directly. Memoization and tabulation are two common approaches to implementing DP. Memoization involves storing the results of subproblems in a table and reusing them when needed, while tabulation involves filling in a table iteratively based on previously computed values. As you practice more DP problems, you'll start to develop an intuition for how to break down complex problems and apply dynamic programming techniques.