Linear applications and eigenvalues

Related lectures (28)

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

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

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

Explores basis transformation, eigenvalues, and linear operators in inner product spaces, emphasizing their significance in Quantum Mechanics.

Covers the organization of linear algebra course and exercises for civil engineering and environmental sciences students.

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

Explores vector spaces, linear transformations, matrices, eigenvalues, inner products, and operators.

Covers the reduction of non-diagonalizable matrices and their geometric interpretations.

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

Explains the diagonalization of matrices, criteria, and significance of distinct eigenvalues.