Chaos and Lyapunov Exponents: Analyzing Predictability

Related lectures (28)

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

Explores direct methods for solving linear systems of equations, including Gauss elimination and LU decomposition.

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

Covers the theorems related to orthogonal projection and orthonormal bases.

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

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

Explores spectral and singular value decompositions of matrices.

Explores orthogonal and orthonormal bases, Gram-Schmidt process, and orthogonal polynomials in physics.

Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.

Explains orthogonal families, bases, and projections in vector spaces.