Gram-Schmidt Process: Orthogonal Vectors

Related lectures (29)

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

Covers the concept of 3D vector space, scalar product, bases, orthogonality, and projections.

Introduces the first step in finding an orthogonal/orthonormal base in a vector space.

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

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

Explores orthogonal matrices and transformations, emphasizing preservation of norms and angles.

Explores orthogonality between vectors and subspaces, demonstrating practical implications in matrix operations.

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

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

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