Iterative Methods for Linear Equations

Related lectures (27)

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

Explores eigenvalues, eigenvectors, and methods for solving linear systems with a focus on rounding errors and preconditioning matrices.

Covers the formulation of linear systems, direct and iterative methods for solving them, and the cost of LU factorization.

Explores quadratic forms, symmetric bilinear forms, and their properties.

Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.

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

Covers iterative methods for solving linear systems and discusses convergence criteria and spectral radius.

Explores harmonic forms on Riemann surfaces and the uniqueness of solutions to harmonic equations.

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

Explores the classification of quadratic forms based on eigenvalues and orthogonal diagonalization of symmetric matrices.