Linear Algebra: Applications and Algorithms

Related lectures (29)

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

Covers derivability, linear applications, composition of functions, and the gradient vector.

Introduces orthogonal bases, projection onto subspaces, and the Gram-Schmidt process in linear algebra.

Covers matrix multiplication, properties, and inverses in linear algebra.

Covers the decomposition of matrices into triangular blocks and spectral decomposition.

Covers the resolution of linear systems and its link to optimization problems.

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

Covers fundamental linear algebra concepts like vector spaces and eigenvalues.

Introduces the EPFL course on Signals and Systems, covering course structure, evaluation, and key engineering concepts.

Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.