Factorisation QR: Gram-Schmidt Process

Related lectures (24)

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Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.

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

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

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

Explores Gram-Schmidt process, orthogonal projection, QR factorization, and least squares solutions for linear systems.

Introduces the Gram-Schmidt algorithm, QR factorization, and the method of least squares.

Explores matrix equivalence theorems for systems of equations and least squares solutions.

Introduces orthogonal families, orthonormal bases, and projections in linear algebra.

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.