Matrix Chain Multiplication using Dynamic Programming | A visutorial with table-filling visualized!

The matrix chain multiplication problem is to find the minimum number of multiplications required to multiply a chain of matrices. The video explains the dynamic programming (DP) based approach to solve the problem. The iterative version of the code is discussed. Finally, the video ends with a running animation of how the DP table is filled in real-time, helping to build intuition and visualization.
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Timestamps:
Channel intro: (00:00)
Introduction to the problem: (00:05)
What is a matrix chain?: (01:50)
Number of multiplications depends on order: (02:19)
Greedy strategy?: (04:13)
Starting to think of DP: (05:28)
Recurrence relations: (07:54)
Recursive bruteforce solution: (09:16)
Iterative bottom-up solution: (11:08)
An animated running example: (13:43)

[lokendrabohra]

Watched it thrice and realized its the most crystal clear explanation available on youtube! Just a suggestion: Include a small test case and tracing of that test case so as to validate the Recurrence relation.

[TheAlgorithmicEye]

Some problems from SPOJ based on this concept:

[lokendrabohra]

Didn't understand how you generalized at 6:00 - 6:36 that The last step whould have combined 2 subproducts (a prefix of the chain and the suffix). What if there's a case Such as: M = ABCD and if we multiply them as A(BC)D results in the minimum cost? How does that generalization apply here?