Optimal Decision Making: The Simplex Method

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Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Explores linear combinations of vectors and matrices in Rn, demonstrating geometric interpretations and matrix operations.

Explores the diagonalization of matrices through eigenvalues and eigenvectors, emphasizing the importance of bases and subspaces.

Covers the diagonalization of matrices using eigenvectors and eigenvalues.

Explores matrices, inverses, and their applications in linear algebra, emphasizing composition and transformation properties.

Explains linear independence, basis, and matrix rank with examples and exercises.

Covers linear transformations using matrices, focusing on linearity, image, and kernel.

Explores linear algebra concepts through examples and theorems, focusing on matrices and their operations.

Covers LU factorization, linear independence, and matrix equations.

Covers matrix eigenvalues, eigenvectors, and their linear independence.