Orthogonal Projections and Reflections in 2D

Related lectures (28)

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

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

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

Explores projections on lines and symmetries in 2D space, emphasizing fixed points and symmetric matrices.

Explores subspaces, spectra, and projections in linear algebra, including symmetric matrices and orthogonal projections.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Explores the spectral decomposition of symmetric matrices, including diagonalization and orthogonal basis change matrices.

Delves into singular value decomposition and its applications in linear algebra.

Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.