Singular Values, Fundamental Theorem

Related lectures (26)

Page 1 of 3

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

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

Explores orthogonality, Gram-Schmidt process, dot products, and solution minimization in systems.

Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.

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

Covers orthogonal linear maps, orthogonal matrices, invertibility, and least squares solutions in Euclidean spaces.

Introduces orthogonal bases, projection onto subspaces, and the Gram-Schmidt process in linear algebra.

Explores orthogonal and orthonormal bases, Gram-Schmidt process, and orthogonal polynomials in physics.

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

Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.