Algorithms Analysis Practice Test

Greedy algorithms operate under the principle of making the locally optimal choice at each stage with the hope of finding a global optimum. However, this approach does not guarantee an optimal solution for all problems. In many cases, the choices made at each step can lead to a suboptimal solution, especially if the problem does not exhibit the properties that make greedy choices lead to a globally optimal result.

For instance, classic problems like the Knapsack Problem or the Traveling Salesman Problem illustrate scenarios where greedy algorithms fail to produce the best possible outcome. These problems require a more holistic approach, like dynamic programming or backtracking, to ensure that the overall solution is optimal. Hence, the primary limitation of greedy algorithms is that they do not yield optimal solutions for all problems, making option B the correct answer.

Other options might highlight aspects of greedy algorithms, such as their efficiency or implementation specifics, but they do not capture the fundamental issue that restricts the applicability of greedy strategies across diverse problem types.