Singular Value Decomposition: Applications and Interpretation

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Explores properties of 3x3 matrices with real coefficients and determinant calculation methods.

Covers the Singular Value Decomposition (SVD) in detail, including properties of matrices and system linearity.

Introduces orthogonal families, orthonormal bases, and projections in linear algebra.

Explores vector subspaces in R4, symmetric matrices, basis vectors, and canonical forms.

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

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

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

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

Explores linear applications in R² and matrix representation, including basis, operations, and geometric interpretation of transformations.

Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.