动态规划通常被称为记忆!

Memoization is the top-down technique(start solving the given problem by breaking it down) and dynamic programming is a bottom-up technique(start solving from the trivial sub-problem, up towards the given problem) DP finds the solution by starting from the base case(s) and works its way upwards. DP solves all the sub-problems, because it does it bottom-up Unlike Memoization, which solves only the needed sub-problems DP has the potential to transform exponential-time brute-force solutions into polynomial-time algorithms. DP may be much more efficient because its iterative On the contrary, Memoization must pay for the (often significant) overhead due to recursion.

简单来说， 记忆法使用自顶向下的方法来解决问题，即从核心(主要)问题开始，然后将其分解为子问题，并以类似的方式解决这些子问题。在这种方法中，同一子问题可能会多次出现，消耗更多的CPU周期，从而增加时间复杂度。而在动态规划中，同一子问题不会求解多次，而是利用其先验结果来优化解。

动态规划(DP)和记忆化之间有一些相似之处，在大多数情况下，您可以通过记忆实现动态规划过程，反之亦然。但它们确实有一些区别，你应该在决定使用哪种方法时查看它们:

Memoization is a top-down approach during which you decompose a big problem into smaller-size subproblems with the same properties and when the size is small enough you can easily solve it by bruteforcing. Dynamic Programming is a bottom-up approach during which you firstly calculate the answer of small cases and then use them to construct the answer of big cases. During coding, usually memoization is implemented by recursion while dynamic programming does calculation by iteration. So if you have carefully calculate the space and time complexity of your algorithm, using dynamic-programming-style implementation can offer you better performance. There do exist situations where using memoization has advantages. Dynamic programming needs to calculate every subproblem because it doesn't know which one will be useful in the future. But memoization only calculate the subproblems related to the original problem. Sometimes you may design a DP algorithm with theoretically tremendous amount of dp status. But by careful analyses you find that only an acceptable amount of them will be used. In this situation it's preferred to use memoization to avoid huge execution time.

动态规划是一种求解给定问题的算法范式 将复杂问题分解为子问题并存储结果 以避免再次计算相同的结果。

http://www.geeksforgeeks.org/dynamic-programming-set-1/

记忆是一种跟踪以前解决的解决方案的简单方法(通常实现为哈希键值对，而不是通常基于数组的制表)，这样当它们再次遇到时就不会重新计算。它可以在自底向上或自顶向下的方法中使用。

请参阅关于记忆和制表的讨论。

动态规划是一种通过求解递归关系/递归并通过制表或记忆存储先前找到的解来解决某些类型问题的方法。记忆是一种跟踪以前解决问题的解决方案的方法，可以与任何对于给定输入集具有唯一确定性解决方案的函数一起使用。

从维基百科:

记忆有关

在计算中，记忆是一种主要使用的优化技术 通过函数调用来加速计算机程序，避免重复 对先前处理过的输入的结果的计算。

动态规划

在数学和计算机科学中，动态规划是一种方法 把复杂的问题分解成更简单的问题 子问题。

当把一个问题分解成更小/更简单的子问题时，我们经常会不止一次遇到相同的子问题——所以我们使用Memoization来保存以前的计算结果，这样我们就不需要重复它们了。

动态编程经常遇到使用内存是有意义的情况，但您可以使用任何一种技术而不必使用另一种技术。

这是一个记忆和DP从斐波那契数问题写在Java的例子。

这里的动态编程不涉及递归，因为它不受执行堆栈的限制，所以结果更快，可以计算出更高的值。

public class Solution {

 public static long fibonacciMemoization(int i) {
    return fibonacciMemoization(i, new long[i + 1]);
 }

 public static long fibonacciMemoization(int i, long[] memo) {
    if (i <= 1) {
        return 1;
    }
    if (memo[i] != 0) {
        return memo[i];
    }
    long val = fibonacciMemoization(i - 1, memo) + fibonacciMemoization(i - 2, memo);
    memo[i] = val;
    return val;
 }

 public static long fibonacciDynamicPrograming(int i) {
    if (i <= 1) {
        return i;
    }
    long[] memo = new long[i + 1];
    memo[0] = 1;
    memo[1] = 1;
    memo[2] = 2;
    for (int j = 3; j <= i; j++) {
        memo[j] = memo[j - 1] + memo[j - 2];
    }
    return memo[i];
 }

 public static void main(String[] args) {
    System.out.println("Fibonacci with Dynamic Programing");
    System.out.println(fibonacciDynamicPrograming(10));
    System.out.println(fibonacciDynamicPrograming(1_000_000));

    System.out.println("Fibonacci with Memoization");
    System.out.println(fibonacciMemoization(10));
    System.out.println(fibonacciMemoization(1_000_000)); //stackoverflow exception
 }
}

在动态规划中，

没有递归的开销，维护表的开销也更少。 表访问的规则模式可用于减少时间或空间需求。

在记忆中,

有些子问题不需要解决。