Linear Algebra: Applications and Algorithms

Related lectures (26)

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Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.

Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.

Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.

Explores the Cholesky factorization method for symmetric positive definite matrices.

Covers direct methods for solving linear systems in numerical analysis.

Explores spectral and singular value decompositions of matrices.

Covers linear systems, diagonal and triangular matrices, and LU factorization.