Orthogonality and Gram-Schmidt Process

Related lectures (29)

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

Covers orthogonal linear maps, orthogonal matrices, invertibility, and least squares solutions in Euclidean spaces.

Explores the fundamental theorem on singular values and the formation of orthonormal bases from eigenvectors.

Covers Dirac's notation in linear algebra and the tensor product concept in Hilbert spaces.

Explains orthogonal projection on a vector subspace in Euclidean space.

Introduces orthogonal sets and bases, discussing their properties and linear independence.

Explores orthogonality, scalar product, and orthonormal bases in vector spaces.

Explores orthogonality, dot product properties, vector norms, and angle definitions in vector spaces.

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

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