Dynamic Programming: Longest Common Subsequence

Related lectures (28)

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Delves into dynamic programming with a focus on Matrix Chain Multiplication and the longest common subsequence problem.

Explores optimal substructure and recursive formula in matrix chain multiplication using dynamic programming.

Covers dynamic programming techniques for solving the rod cutting and matrix chain multiplication problems.

Discusses matrix multiplication using divide-and-conquer techniques and introduces Strassen's algorithm for improved efficiency.

Explores dynamic programming with a focus on optimizing Matrix Chain Multiplication.

Explores dynamic programming with a focus on matrix-chain multiplication and the importance of optimal substructure.

Introduces dynamic programming with a focus on rod cutting and matrix chain multiplication.

Explores properties of 3x3 matrices with real coefficients and determinant calculation methods.

Covers the basics of Numpy, including importing the library, creating arrays, and performing linear algebra operations.

Covers the basics of parallelism, including physical examples, historical context, multicore era, and parallel collections in Scala.