Greedy algorithms are a type of algorithmic paradigm that builds a solution incrementally by making the locally optimal choice at each step, hoping to find a global optimum. The fundamental idea behind greedy algorithms is to make the most favorable decision at each stage without considering the overall problem, which can lead to a quick and straightforward solution. These algorithms are particularly effective for certain optimization problems where the locally optimal choices lead to a globally optimal solution. Common examples of problems that can be solved using greedy algorithms include the activity selection problem, where the goal is to select the maximum number of compatible activities, and the minimum spanning tree problem, where algorithms like Prim's and Kruskal's efficiently find a spanning tree with the smallest total edge weight. However, greedy algorithms do not guarantee optimal solutions for all problems, and their effectiveness depends on the problem's structure. It is essential to analyze the problem to determine if a greedy approach is appropriate. If a problem exhibits the greedy-choice property and optimal substructure, a greedy algorithm can yield the best solution efficiently. Understanding when to apply greedy algorithms and recognizing their limitations is crucial for algorithm design and problem-solving in various fields, including computer science, operations research, and artificial intelligence.