Eigen Library for Linear Algebra

Related lectures (23)

Page 1 of 3

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

Explores matrix operations, linear systems, solutions, and the span of vectors in linear algebra.

Covers linear equations, vectors, and matrices, exploring their fundamental concepts and applications.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

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

Covers linear applications, matrices, transformations, and the principle of superposition.

Covers the basics of Numpy, including importing the library, creating arrays, and performing linear algebra operations.

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

Covers the definitions and properties of matrices, including matrix operations and determinants.

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