Orthogonal Basis of Subspaces

Related lectures (25)

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

Covers orthogonal bases and Hermitian forms in linear algebra.

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

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

Introduces orthogonal families, orthonormal bases, and projections in linear algebra.

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

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

Explores orthogonality, Gram-Schmidt process, dot products, and solution minimization in systems.

Explores the Gram-Schmidt process to obtain orthogonal vectors and bases.

Emphasizes the importance of using orthogonal bases in linear algebra for representing linear transformations.