Eigen Library for Linear Algebra

Related lectures (23)

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Introduces Eigen and Pybind libraries for linear algebra operations and C++ function binding to Python.

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

Explores linear combinations of vectors and matrices in Rn, demonstrating geometric interpretations and matrix operations.

Explores linear algebra concepts through examples and theorems, focusing on matrices and their operations.

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

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.

Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.

Explores eigenvalues and eigenvectors, demonstrating their importance in linear algebra and their application in solving systems of equations.

Explores linear combinations, matrix-vector product, and matrix equation solutions.

Covers matrices, linear applications, vector spaces, and bijective functions.