Gram-Schmidt Process and QR Decomposition

Related lectures (25)

Page 2 of 3

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

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

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

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

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

Covers orthogonal bases, Gram-Schmidt method, linear independence, and orthonormal matrices in vector spaces.

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

Covers matrix operations, scalar product, orthogonality, and bases in vector spaces.

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