Linear Algebra: Applications and Algorithms

Related lectures (26)

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Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

Explains the Gaussian elimination algorithm with pivoting and LU factorization for linear systems.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Covers the Singular Value Decomposition (SVD) of a matrix and its applications.

Covers the formulation of linear systems, direct and iterative methods for solving them, and the cost of LU factorization.

Introduces Singular Value Decomposition (SVD) in linear algebra, covering matrix factorization and properties with practical examples.

Covers the decomposition of matrices into triangular blocks and spectral decomposition.

Covers the construction of an iterative method for linear systems by decomposing a matrix A into P, T, and P_A.

Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.