Linear systems resolution

Related lectures (27)

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

Explores direct methods for solving linear equations and the impact of errors on solutions and matrix properties.

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

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Covers the Choleski factorisation method for solving linear systems efficiently.

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

Covers the Cholesky decomposition of a symmetric positive definite matrix and its applications.

Explores the properties of invertible matrices, including unique solutions and linear independence.

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

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