Competitive programming problems can be broadly categorized into several types, each requiring a different set of techniques and strategies. Dynamic programming (DP) problems are among the most challenging and involve breaking down a problem into subproblems and solving them efficiently using memoization or tabulation. These problems often involve optimization tasks or finding the best possible outcome under certain constraints. Graph theory problems, on the other hand, focus on traversing or analyzing graphs, and common algorithms include BFS, DFS, Dijkstra's, and Floyd-Warshall. Graph problems are often seen in pathfinding, network flows, or connectivity-related tasks. String manipulation problems are another common type and can involve tasks like finding substrings, pattern matching, or string transformations. Efficient string algorithms like KMP or Z-algorithm are often required for solving these problems. Combinatorics problems involve counting the number of ways to arrange or select items, and they often require knowledge of binomial coefficients, permutations, or combinations. Lastly, greedy algorithm problems require making a series of locally optimal choices to achieve the best overall result. These problems are often simpler but require careful attention to detail. Understanding these problem types and practicing with each will help you tackle a wide range of challenges in competitive programming.