Greedy algorithms are an important tool in competitive programming, particularly for problems where making a series of locally optimal choices leads to a globally optimal solution. Unlike dynamic programming, where you often have to explore multiple paths or states, greedy algorithms aim to make the best possible choice at each step, with the hope that this will lead to the correct overall solution. The key to solving greedy algorithm problems is to determine whether the problem's structure allows for such an approach. For example, classic greedy algorithm problems include interval scheduling, where you're given a set of intervals and need to select the maximum number of non-overlapping intervals. In this case, the greedy approach of always choosing the interval that finishes the earliest works because it leaves more room for future intervals. Other problems where greedy algorithms shine include coin change problems (using the fewest coins to make a specific amount), Huffman encoding (for data compression), and certain optimization problems where you're trying to minimize or maximize a result. The efficiency of greedy algorithms often makes them a great choice when the input size is large and an O(n log n) or O(n) solution is required. However, not all problems can be solved greedily—some require more complex approaches like dynamic programming—so it's important to recognize when a greedy algorithm is appropriate.