Linear Applications and Eigenvectors

Related lectures (26)

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

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

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.

Explores orthogonality, norms, and distances in vector spaces for solving linear systems.

Introduces orthogonality between vectors, angles, and orthogonal complement properties in vector spaces.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Explores polynomial operations, properties, and subspaces in vector spaces.

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

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