Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Primitive Matrices and Spectral Properties in Networked Control Systems
Graph Chatbot
Related lectures (45)
Non-Negative Definite Matrices and Covariance Matrices
Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.
Spectral Theorem Recap
Revisits the spectral theorem for symmetric matrices, emphasizing orthogonally diagonalizable properties and its equivalence with symmetric bilinear forms.
Matrix Computations: Eigenvalues and Eigenvectors
Explores the complexity of matrix computations, focusing on eigenvalues and eigenvectors of symmetric matrices and the challenges in computing them.
Eigenvalues and Eigenvectors of Markov Chains
Explores eigenvalues and eigenvectors of Markov chains, focusing on convergence rates and matrix properties.
Matrices and Networks
Explores the application of matrices and eigendecompositions in networks.
Networked Control Systems: Consensus and Connectivity
Explores consensus algorithms and connectivity properties in networked control systems.
Canonical Correlation Analysis: Overview
Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.
Diagonalization of Linear Transformations
Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.
Symmetric Matrices: Diagonalization
Log in to Mediaspace to watch this video
Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.
Matrix Diagonalization: Spectral Theorem
Log in to Mediaspace to watch this video
Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.
Eigenvalues and Eigenvectors Decomposition
Log in to Mediaspace to watch this video
Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.
Symmetric Matrices: Eigenvalues and Eigenvectors
Log in to Mediaspace to watch this video
Explores the diagonalization of symmetric matrices using eigenvectors and eigenvalues, emphasizing orthogonality and real eigenvalues.
Symmetric Matrices: Properties and Decomposition
Log in to Mediaspace to watch this video
Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.
Diagonalization in Symmetric Matrices
Log in to Mediaspace to watch this video
Explores diagonalization in symmetric matrices, emphasizing orthogonality and orthonormal bases.
Decomposition Spectral: Symmetric Matrices
Log in to Mediaspace to watch this video
Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.
Characteristic Polynomials and Similar Matrices
Log in to Mediaspace to watch this video
Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.
Diagonalization of Symmetric Matrices
Log in to Mediaspace to watch this video
Explores the diagonalization of symmetric matrices through orthogonal decomposition and the spectral theorem.
Diagonalization of Symmetric Matrices
Log in to Mediaspace to watch this video
Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.
Eigenvalues and Fibonacci Sequence
Log in to Mediaspace to watch this video
Covers eigenvalues, eigenvectors, and the Fibonacci sequence, exploring their mathematical properties and practical applications.
Matrices and Quadratic Forms: Key Concepts in Linear Algebra
Log in to Mediaspace to watch this video
Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.
Previous
Page 1 of 3
Next
