Linear Algebra: Best Approximation and Properties

Related lectures (25)

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Explores orthogonal base change in linear algebra, focusing on matrices and transformations.

Covers the proof of the symmetry of a matrix through exercises involving symmetric matrices.

Explores linear applications in vector spaces, emphasizing subspaces and properties of linear maps.

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

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Explores the diagonalization of symmetric matrices through orthogonal decomposition and the spectral theorem.

Explains the concept of inertia benchmark, focusing on main axes and diagonal elements of the inertia tensor.

Delves into singular value decomposition and its applications in linear algebra.

Covers symmetric matrices, eigenvalues, and diagonalization process for spectral theorem applications.

Discusses quadratic forms, matrix diagonalization, and their applications in optimization problems.