1. Chapter 5 MATRIX ALGEBRA: DETEMINANT, REVERSE, EIGENVALUES

2. The determinant of a matrix is a fundamental concept of linear algebra that provides existence and uniqueness results for linear systems of equations; det A = |A| LU factorization: A = LU, the determinant is |A| = |L||U| (5.1) Doolittle method: L = lower triangular matrix with l ii = 1  |L| = 1  |A| = |U| = u 11 u 22 …u nn Pivoting: each time we apply pivoting we need to change the sign of the determinant  |A| = (-1) m u 11 u 22 …u nn Gauss forward elimination with pivoting: 5.1 The Determinant of a Matrix

3. Procedure for finding the determinant following the elimination method

4. If A = BC  |A| = |B||C| (A, B, C – square matrices) |A T | = |A| If two rows (or columns) are proportional, |A| = 0 The determinant of a triangular matrix equals the product of its diagonal elements A factor of any row (or column) can be placed before the determinant Interchanging two rows (or columns) changes the determinant sign The properties of the determinant

5. Definition: A -1 is the inverse of the square matrix A, if (5.5) I – identity matrix A -1 = X (denote)  AX = I (5.6) LU factorization, Doolittle method: A = LU LY = I, UX = Y (5.7) Here Y = {y ij }, LY = I – lower triangular matrix with unity diagonal elements, i = 1,2,…,n; so for j from 1 to n Also, X = {xij} vector, i = 1,2,…,n  5.2 Inverse of a Matrix

6. Procedure for finding the inverse matrix following the LU factorization method

7. If Ax = b  x = A -1 b (A - matrix nxn, b, x – n-dimensional vectors) If AX = B  X = A -1 B (A – matrix nxn, B,X – matrices nxm) – more often case.  We can write down this system as Ax i = b i (i = 1,2,…,m) x i, b i – vectors, i th rows of matrices X, B, respectively

8. X = A -1 B calculating procedure following the method of LU factorization

9. Definition: let A be an nxn matrix For some nonzero column vector x it may happen, for some scalar λ Ax = λx (5.9) Then λ is an eigenvalue of A, x is eigenvector of A, associated with the eigenvalue λ; the problem of finding eigenvalue or eigenvector – eigen problem Eq.(5.9) can be written in the form Av = λIv or (A – λI)x = 0 If A is nonsingular matrix  inverse exists  det A ≠ 0  x = (A – λI) -1 0 = 0 Hence, not to get zero solution x = 0, (A – λI) must not be nonsingular, i.e. det A = 0: (5.10) (5.11) 5.3 Eigenvalues and Eigenvectors

10. Eq. (5.11) -n th order algebraic equation with n unknowns (algebraic as well as complex) Applying some numerical calculation, we can find λ But when n is big, expanding to Eq.(5.11) is not the easy way to solve  this method is not used much Here: Jacobi and QR methods for finding eigenvalues

11. Jacobi method is the direct method for finding eigenvalues and eigenvectors in case when A is the symmetric matrix diag(a 11,a 12,…,a nn ) – diagonal matrix A e i – unit vector with i th element = 1 and all others = 0 The properties of egenvalues and eigenvectors: 1. if x – eigenvector of A, then ax is also eigenvector (a = constant) 2. if A – diagonal matrix, then a ii are eigenvalues, e i is eigenvector 3. if R, Q are orthogonal matrices, then RQ is orthogonal 4. if λ – eigenvalue of A, x – eigenvector of A, R – orthogonal matrix, then λ is eigenvalue of R T AR, R T x is its eigenvector. R T AR is called similarity transformation 5. Eigenvalues of a symmetric matrix are real number 5.3.1 Jacobi method

12. Jacobi method uses the properties mentioned above A – arbitrary matrix, R i – orthogonal matrix, R i T AR i – similarity transformation (5.12) - diagonal matrix  following the 2 nd property, the eigenvector is e i We can write the eq.(5.12) as Following the 3 rd property, R 1 R 2 …R n is orthogonal matrix; the 4 th property – it has the same eigenvalues as A The eigenvector of A is Then the matrix consisting of x i as columns Having found X, we find eigenvetors x i

13. Let’s consider 2-dimensional matrix example The orthogonal matrix is C, S – notation for cosθ, sinθ respectively We need to choose θ so that the above matrix becomes diagonal  If a 11 ≠a 22, then If a 11 =a 22, then θ=π/4 With this θ, R T AR is diagonal matrix, its diagonal elements are eigenvalues of A, and R the eigenvecotrs matrix

14. If A is nxn matrix, a ij – its non-diagonal elements with the largest absolute value, then orthogonal matrix R k and θ: (5.15) (5.16)

15. Then if we calculate R T k AR k, after transformation elements a* ij (5.17) Then again repeat the process, selecting the largest absolute valued non-diagonal elements and reducing them to zero Convergence condition: (5.18)

16. Jacobi method calculation procedure

17. Jacobi method model (1)

18. Jacobi model (2)

19. To find the eigenvalues and eigenvectors of a real matrix A, three methods are combined:  Pretreatment -Householder transformation  Calculation of eigenvalues - QR method  Calculation of eigenvectors -inverse power method 5.3.2 QR method

20. Series of orthogonal transformations A k+1 = P k T A k P k For k = 1, …, n-2, starting from initial matrix A = A 1 and applying the similarity transformation until we get three-diagonal matrix A n-1 A three-diagonal matrix Matrix P k, n-dimensional vector u k (1) Householder transformation

21. Matrix P k, n-dimensional vector u k u k T u k = 1 = I. P k – symmetric matrix and through it is also orthogonal Orthogonal matrix satisfies the following statement:  If we have two column-vectors x, y, x≠y and ||x|| = ||y||, and if we assign then

22. In case k = 1 Here Since ||b1|| = ||s1e1||

23. Jacobi method model (1)

24. Jacobi model (2)