//_divide & conquer + memoization == top-down dynamic programming
Well, recursion+memoization is precisely a specific "flavor" of dynamic programming: dynamic programming in accordance with top-down approach.
More precisely, there's no requrement to use recursion specifically. Any divide & conquer solution combined with memoization is top-down dynamic programming. (Recursion is LIFO flavor of divide & conquer, while you can also use FIFO divide & conquer or any other kind of divide & conquer).
So it is more correct to say that
divide & conquer + memoization == top-down dynamic programming
Also, from a very formal point of view, if you implement a divide & conquer solution for a problem that does not generate repetitive partial solutions (meaning that there's no benefit in memoization), then you can claim that this divide & conquer solution is a degenerate example of "dynamic programming".
However, dynamic programming is a more general concept. Dynamic programming can use bottom-up approach, which is different from divide & conquer+memoization.

Dynamic programming is used where we have problems,
which can be divided into similar sub-problems,
so that their results can be re-used.

 n	Worst Case Comment
<=[10..11]	O(n!),O(n^6) Enumerating Permutations
<=[15..18]	O(2^n X n^2) DP Travel and Salesman Problem
<=[18..22]	O(2^n X n) DP With Bitmask Technique
<=100	O(n^4) DP With 3 dimention + O(n)loop
<=400	O(n^3) Floyd Warshall's
<=2000	O(n^2Log(n)) 2-Nested Loop +tree-related DS
<=10,000	O(n^2) Bubble/Selection/Insertion Sort
<=1M	O(nLog(n)) Merge Sort , Building Segment Tree
<=100M	O(n),O(Log(n)),O(1) Bottleneck
Source - From Competitive Programming 3 Book

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Dynamic programming amounts to breaking down an optimization problem into simpler sub-problems,
and storing the solution to each sub-problem so that each sub-problem is only solved once.