Numerical Analysis 4.1. Linear Fixed Point Iteration скачать в хорошем качестве

Numerical Analysis 4.1. Linear Fixed Point Iteration

This video is a lecture on Numerical Analysis (Section 4.1), focusing on iterative techniques for solving linear systems through linear fixed point iteration. 1. Eigenvalues and Spectral Radius The lecture begins by reviewing fundamental concepts from linear algebra necessary for understanding iterative convergence: Eigenvalues (λ) and Eigenvectors (x): Defined by the equation Ax=λx. These are found using the characteristic equation det(A−λI)=0 [02:01]. Spectral Radius (ρ(A)): This is the maximum absolute value of the eigenvalues of a matrix. Geometrically, it represents the radius of the smallest circle centered at the origin that contains all eigenvalues in the complex plane [03:17]. Property: The spectral radius of a matrix is always less than or equal to any matrix norm (ρ(A)≤∥A∥) [03:37]. 2. Linear Fixed Point Iteration The core topic is the solution of the linear fixed point equation: x=Tx+c, where T is a square matrix and c is a vector [05:01]. Iteration Sequence: The sequence is defined as x k+1 =Tx k +c [05:20]. Convergence Theorem: The iteration converges to a unique solution for any initial vector x 0 if and only if the spectral radius of the transition matrix T is less than one (ρ(T) 1) [23:31]. Global Convergence: Unlike nonlinear problems, this condition provides "global convergence," meaning it works regardless of the starting point [23:46]. 3. Matrix Series (Geometric Series) The instructor explains the matrix version of the geometric series, ∑A k [13:20]: The series converges if and only if ρ(A) < 1. When it converges, the sum is equal to (I−A) −1 [14:04]. This result is used to prove that if ρ(T) 1, then the matrix (I−T) is invertible, ensuring a unique solution to the system exists [14:52]. 4. Error Estimates and Stability The lecture concludes with practical considerations for computation: Rate of Convergence: If ∥T∥ 1, the error decreases as ∥T∥ k . Thus, a smaller matrix norm leads to faster convergence [27:29]. Rounding Errors: The method is numerically stable. If small rounding errors (ϵ) occur during each step, the total error remains bounded and does not grow infinitely, provided ∥T∥ 1 [32:11].