Greedy algorithms work when local decisions lead to global optima. If your algorithm fails, it might not satisfy the greedy-choice property.

Greedy algorithms often fail because they rely on making the locally optimal choice at each step, assuming that this will lead to a global optimum. However, this strategy only works if the problem satisfies two key properties: the greedy-choice property and optimal substructure. If the problem doesn’t meet these properties, a greedy solution might not work. To check if a greedy approach is appropriate, try to prove that making a local choice will lead to the global optimum in all cases. For problems where greedy algorithms fail, dynamic programming or backtracking might be better alternatives. Understanding when to apply greedy algorithms—and when not to—will improve your problem-solving skills in competitive programming.