Construction of an Iterative Method

Related lectures (25)

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Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.

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

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Explores the Cholesky factorization method for symmetric positive definite matrices.

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.

Explores LU decomposition for solving linear systems and the associativity of operations.

Covers the Singular Value Decomposition (SVD) of a matrix and its applications.

Explores the Jacobi method and diagonalization techniques, including similarity transformation, power methods, and QR decomposition.

Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.