Matrix Factorization: LU Decomposition

Related lectures (26)

Page 2 of 3

Covers the LU decomposition algorithm, transforming a matrix into L and U.

Covers elementary matrices, matrix factorization, and LU decomposition.

Explores the Cholesky factorization method for symmetric positive definite matrices.

Covers direct methods for solving linear systems in numerical analysis.

Explores the equivalence between different properties of linear transformations represented by matrices and various matrix operations.

Introduces the Gram-Schmidt algorithm, QR factorization, and the method of least squares.

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

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

Explains Gaussian elimination and LU factorization for solving linear systems of equations.

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.