Linear Algebra: Applications and Algorithms

Related lectures (29)

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

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

Explains the Principal Axes Theorem for symmetric matrices and quadratic forms, showing the existence of orthogonal matrices for diagonalization.

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

Introduces orthogonal and symmetric matrices in linear algebra with practical examples.

Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Covers the process of finding orthogonal bases and spectral decomposition of symmetric matrices.

Explores projections on lines and symmetries in 2D space, emphasizing fixed points and symmetric matrices.

Explores orthogonal matrices and transformations, emphasizing preservation of norms and angles.