Systems of Differential Equations

Related lectures (27)

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Explores eigenvalues, eigenvectors, diagonalization, and spectral theorem in linear algebra.

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Explores the diagonalization of matrices through eigenvalues and eigenvectors, emphasizing the importance of bases and subspaces.

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

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

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

Covers the method of diagonalization for determining if a non-square matrix A is diagonalizable.

Explores the diagonalization of linear maps by finding a basis formed by eigenvectors.

Explores the properties and examples of diagonalizable matrices, emphasizing the relationship between eigenvectors and eigenvalues.

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