Make sure you have read and understood what 0-1 Knapsack Problem is before moving on with this question.
No, using a greedy strategy with the greedy parameter being the value per unit weight of the items does not always yield an optimal solution for the 0-1 knapsack problem. The 0-1 knapsack problem is a classic optimization problem where a knapsack has a fixed capacity, and you need to select items with the maximum total value without exceeding this capacity.
While the greedy strategy based on value per unit weight seems intuitive, it can lead to suboptimal solutions in certain cases. Consider a scenario where you have two items: Item A with a high value but also high weight, and Item B with a slightly lower value but much lower weight. If the capacity of the knapsack is such that you can only choose one item, the greedy strategy might choose Item A due to its higher value per unit weight. However, the optimal solution might be to choose Item B because it allows fitting more value within the capacity constraint.
In essence, the greedy strategy based solely on value per unit weight doesn't consider the interplay between items and the overall capacity constraint. It can overlook combinations of items that collectively offer higher value within the capacity constraint. To guarantee an optimal solution for the 0-1 knapsack problem, dynamic programming algorithms, which consider all possible combinations of items and their weights, are commonly employed. These algorithms ensure finding the globally optimal solution by systematically exploring all possibilities rather than making locally optimal choices at each step.