Greedy algorithms fail when they make locally optimal choices that don’t lead to a globally optimal solution.

Greedy algorithms work by making the best local choice at each step, hoping that these local choices will lead to a globally optimal solution. However, they can fail when the problem requires considering future steps or when local optimality doesn’t guarantee global optimality. Problems like the knapsack problem or scheduling problems are good examples where greedy solutions may fail. In such cases, dynamic programming or backtracking might be more appropriate. To determine if a greedy approach works, check if the problem exhibits the greedy-choice property and optimal substructure. If not, the greedy algorithm may not be the right approach.