Orthogonal Linear Maps

Related lectures (26)

Page 2 of 3

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

Explains orthogonal projection in linear algebra, focusing on transforming non-orthogonal bases into orthogonal ones.

Explains linear independence, basis, and matrix rank with examples and exercises.

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

Explores orthogonality, norms, and distances in vector spaces for solving linear systems.

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

Covers the Gram-Schmidt algorithm for orthonormal bases in vector spaces.

Covers linear applications, matrices, transformations, and the principle of superposition.

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

Explores isometries in Euclidean spaces, including translations, rotations, and linear symmetries, with a focus on matrices.