Calcul de valeurs propres

Related lectures (30)

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

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Explores the diagonalizability of symmetric matrices and their eigenvectors in an orthonormal basis.

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

Discusses eigenvalues, their calculation methods, and their applications in optimization and numerical analysis.

Explores spectral and singular value decompositions of matrices.

Explores diagonalization in symmetric matrices, emphasizing orthogonality and orthonormal bases.

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

Explores eigenvalues and eigenvectors in 3D linear algebra, covering characteristic polynomials, stability under transformations, and real roots.

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.