Convex Optimization: Linear Algebra Review

Related lectures (24)

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Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.

Covers bases, transformations, and matrix decompositions in linear algebra.

Covers the concept of singular values in linear algebra and their properties, including diagonalization and practical examples.

Explains how to find free variables in matrix equations and analyze characteristic polynomials.

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

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

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

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Explores the Cholesky factorization method for symmetric positive definite matrices.

Explores basis transformation, eigenvalues, and linear operators in inner product spaces, emphasizing their significance in Quantum Mechanics.