Gram-Schmidt Process: Orthogonal Vectors & Bases

Related lectures (22)

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Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

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

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

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

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

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

Explores linear dependence and independence of vectors in geometric spaces.

Covers vector spaces, operations, and linear independence with examples from polynomials and functions.

Explains orthogonal families, bases, and projections in vector spaces.

Explains linear independence, bases, and dimension in vector spaces, including the importance of the order of vectors in a basis.