Lectures related to Iterative Solvers: Eigenvalue Problems

Covers the Richardson method for solving linear systems with preconditioned iterative solvers and introduces the gradient method.

Covers linear equations, vectors, and matrices, exploring their fundamental concepts and applications.

Covers matrix equations as linear combinations, vector spaces, and geometric interpretations.

Covers the method of diagonalization for determining if a non-square matrix A is diagonalizable.

Covers eigenvalues, eigenvectors, and the Fibonacci sequence, exploring their mathematical properties and practical applications.

Covers the Jacobi method, Givens rotation, QR decomposition, and diagonalization techniques in computational physics.

Covers eigenvalues, eigenvectors, and characteristic polynomials in matrix transformations.

Covers the Power Method for approximating the largest eigenvalue of a matrix.