Matrix Decomposition: QR Factorization

Related lectures (26)

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

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

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

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

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

Explores spectral and singular value decompositions of matrices.

Covers the LU decomposition algorithm, transforming a matrix into L and U.

Explains the QR factorization theorem and demonstrates the Gram-Schmidt procedure with an example.

Covers Lyapunov exponents, chaos measurement, and perturbation analysis in dynamical systems.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.