How can dynamic programming be applied to optimize matrix multiplication?
- Divide and Conquer
- Dynamic Programming
- Memoization
- Strassen's Algorithm
Dynamic programming can optimize matrix multiplication by breaking down the problem into smaller subproblems and storing their solutions in a table (memoization). This approach reduces redundant computations and improves efficiency compared to naive methods. Techniques like Strassen's algorithm and divide and conquer can also be used, but dynamic programming specifically focuses on efficiently solving overlapping subproblems in matrix multiplication.
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