QR Factorization

Related lectures (26)

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Covers the LU decomposition method applied to linear systems, presenting the system in two steps.

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 the Factorisation QR theorem and the Gram-Schmidt method for orthonormal bases.

Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.

Explores matrix decompositions, algorithms, computational complexity, and predator-prey interactions in numerical linear algebra.

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Introduces QR factorization for matrix decomposition, emphasizing its importance in various applications and the implications of a well-chosen model.

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

Explains orthogonal families, bases, and projections in vector spaces.