Subspace-based approximations

Related lectures (27)

Page 2 of 3

Covers the theorems related to orthogonal projection and orthonormal bases.

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

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

Explains finding the closest point on a plane using orthogonal projection.

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

Explores orthogonal projection calculation and orthonormal bases uniqueness through matrix operations.

Explores vector subspaces in R4, symmetric matrices, basis vectors, and canonical forms.

Covers scalar products, orthogonal vectors, norms, and projections in vector spaces, emphasizing orthonormal families of vectors.

Explores orthogonal complement and projection theorems in vector spaces.

Explains orthogonal matrices, Gram-Schmidt process, and best vector approximation in subspaces.