Computational complexity of matrix multiplication

Related lectures (30)

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Covers matrix operations, including partitioning and block multiplication.

Introduces the concept of vector spaces and covers properties of vectorial subspaces.

Covers the basics of vector spaces, including operational definitions, properties, examples in RN, inner products, norms, and distances.

Covers linear applications, matrices, injectivity, surjectivity, and matrix multiplication properties.

Explains derivatives of composite functions and the chain rule theorem, emphasizing the importance of understanding function composition.

Discusses Strassen's algorithm for matrix multiplication and heaps, covering efficient algorithms and their applications in computer science.

Explores the Divide-and-Conquer algorithm for matrix multiplication, including Strassen's Method and its significance in optimizing time complexity.

Demonstrates the proof of a matrix as a projection matrix in Rn.

Covers the definition and basic operations of matrices, including addition, scalar multiplication, and matrix multiplication.

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