Primitive Matrices and Spectral Properties in Networked Control Systems

Related lectures (28)

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Covers the spectral properties of non-negative matrices and their interpretation in digraphs.

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

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

Covers the calculation of eigenvalues and eigenvectors, emphasizing their significance and applications.

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

Explores the diagonalization of symmetric matrices using eigenvectors and eigenvalues, emphasizing orthogonality and real eigenvalues.

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

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

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.