Which problem is commonly solved using a greedy algorithm?

The Activity Selection problem is a classic example of a problem that can be effectively solved using a greedy algorithm. In this problem, we need to select the maximum number of activities that do not overlap in time, given a set of activities each defined by its start and finish times. The greedy approach works by selecting activities based on their finish times, which ensures that we always pick the next activity that leaves the most room for subsequent activities.

This method is efficient because it makes local optimizations that lead to a global solution—by always choosing the earliest finishing activity that is compatible with those already selected, it maximizes the number of activities chosen. The core essence of a greedy approach is making the most immediate benefit choice without reconsideration, which aligns perfectly with how the Activity Selection problem can be tackled.

In contrast, while other choices may have greedy components, they are not solely reliant on a greedy approach for their optimal solutions. For example, the Knapsack problem generally requires more complex methods, such as dynamic programming, to ensure that the best possible set of items is selected, especially when dealing with item weights and values. The Traveling Salesperson Problem is also known for being NP-hard, typically necessitating more exhaustive or heuristic approaches to find the best solution. The