Eigenvalues: Finding Methods

Related lectures (27)

Page 2 of 3

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

Covers the reduction of a linear application and finding corresponding reduced forms and bases.

Covers bases, transformations, and matrix decompositions in linear algebra.

Introduces eigenspaces in linear algebra through definitions and practical examples of determining eigenspaces for matrices.

Covers matrix kernels, images, linear applications, independence, and bases in vector spaces.

Explores the application of linear algebra in quantum mechanics, emphasizing vector spaces, Hilbert spaces, and the spectral theorem.

Introduces fundamental concepts in linear algebra, such as vector spaces and eigenvalues.

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

Explores matrices, change of bases, eigenvectors, eigenvalues, and vector spaces.

Covers the properties and operations of vector spaces, including addition and scalar multiplication.