贪婪算法是一种算法范式，它逐个构建解决方案，始终选择下一个提供最明显和最直接的好处的部分。因此，选择局部最优也导致全局解决方案的问题最适合贪婪。
例如，考虑分数背包问题。局部最优策略是选择具有最大值与权重比的项目。这个策略也导致全局最优解，因为我们允许取一个项目的分数。
动态规划主要是对普通递归的优化。无论我们在哪里看到重复调用相同输入的递归解决方案，我们都可以使用动态规划对其进行优化。这个想法是简单地存储子问题的结果，这样我们就不必在以后需要时重新计算它们。这种简单的优化将时间复杂度从指数降低到多项式。例如，如果我们为斐波那契数写一个简单的递归解，我们会得到指数时间复杂度，如果我们通过存储子问题的解来优化它，时间复杂度会降低到线性。
以下是贪心方法和动态规划之间的一些主要区别：
| Feature | Greedy method | Dynamic programming |
|---|---|---|
| Feasibility | In a greedy Algorithm, we make whatever choice seems best at the moment in the hope that it will lead to global optimal solution. | In Dynamic Programming we make decision at each step considering current problem and solution to previously solved sub problem to calculate optimal solution . |
| Optimality | In Greedy Method, sometimes there is no such guarantee of getting Optimal Solution. | It is guaranteed that Dynamic Programming will generate an optimal solution as it generally considers all possible cases and then choose the best. |
| Recursion | A greedy method follows the problem solving heuristic of making the locally optimal choice at each stage. | A Dynamic programming is an algorithmic technique which is usually based on a recurrent formula that uses some previously calculated states. |
| Memoization | It is more efficient in terms of memory as it never look back or revise previous choices | It requires dp table for memoization and it increases it’s memory complexity. |
| Time complexity | Greedy methods are generally faster. For example, Dijkstra’s shortest path algorithm takes O(ELogV + VLogV) time. | Dynamic Programming is generally slower. For example, Bellman Ford algorithm takes O(VE) time. |
| Fashion | The greedy method computes its solution by making its choices in a serial forward fashion, never looking back or revising previous choices. | Dynamic programming computes its solution bottom up or top down by synthesizing them from smaller optimal sub solutions. |
| Example | Fractional knapsack . |
0/1 knapsack problem |
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