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### tutorial4 Numerical Linear Algebra (ang)

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Edward Neuman Department of Mathematics Southern Illinois University at Carbondale edneuman@siu.edu This tutorial is devoted to discussion of the computational methods used in numerical linear algebra. Topics discussed include, matrix multiplication, matrix transformations, numerical methods for solving systems of linear equations, the linear least squares, orthogonality, singular value decomposition, the matrix eigenvalue problem, and computations with sparse matrices.

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The following MATLAB functions will be used in this tutorial.

Function
abs chol cond det diag diff eps eye fliplr flipud flops full funm hess hilb imag inv length lu max

Description
Absolute value Cholesky factorization Condition number Determinant Diagonal matrices and diagonals of a matrix Difference and approximate derivative Floating point relative accuracy Identity matrix Flip matrix in left/right direction Flip matrix in up/down direction Floating point operation count Convert sparse matrix to full matrix Evaluate general matrix function Hessenberg form Hilbert matrix Complex imaginary part Matrix inverse Length of vector LU factorization Largest component

2

min norm ones pascal pinv qr rand randn rank real repmat schur sign size sqrt sum svd tic toc trace tril triu zeros

Smallest component Matrix or vector norm Ones array Pascal matrix Pseudoinverse Orthogonal-triangular decomposition Uniformly distributed random numbers Normally distributed random numbers Matrix rank Complex real part Replicate and tile an array Schur decomposition Signum function Size of matrix Square root Sum of elements Singular value decomposition Start a stopwatch timer Read the stopwach timer Sum of diagonal entries Extract lower triangular part Extract upper triangular part Zeros array

!  "# " $  Computation of the product of two or more matrices is one of the basic operations in the numerical linear algebra. Number of flops needed for computing a product of two matrices A and B can be decreased drastically if a special structure of matrices A and B is utilized properly. For instance, if both A and B are upper (lower) triangular, then the product of A and B is an upper (lower) triangular matrix. function C = prod2t(A, B) % Product C = A*B of two upper triangular matrices A and B. [m,n] = size(A); [u,v] = size(B); if (m ~= n) | (u ~= v) error('Matrices must be square') end if n ~= u error('Inner dimensions must agree') end C = zeros(n); for i=1:n for j=i:n C(i,j) = A(i,i:j)*B(i:j,j); end end 3 In the following example a product of two random triangular matrices is computed using function prod2t. Number of flops is also determined. A = triu(randn(4)); flops(0) C = prod2t(A, B) nflps = flops C = -0.4110 0 0 0 nflps = 36 -1.2593 0.9076 0 0 -0.6637 0.6371 -0.1149 0 -1.4261 1.7957 -0.0882 0.0462 B = triu(rand(4)); For comparison, using MATLAB's "general purpose" matrix multiplication operator *, the number of flops needed for computing the product of matrices A and B is flops(0) A*B; flops ans = 128 Product of two Hessenberg matrices A and B, where A is a lower Hessenberg and B is an upper Hessenberg can be computed using function Hessprod. function C = Hessprod(A, B) % Product C = A*B, where A and B are the lower and % upper Hessenberg matrices, respectively. [m, n] = size(A); C = zeros(n); for i=1:n for j=1:n if( j<n ) l = min(i,j)+1; else l = n; end C(i,j) = A(i,1:l)*B(1:l,j); end end We will run this function on Hessenberg matrices obtained from the Hilbert matrix H H = hilb(10); 4 A = tril(H,1); flops(0) B = triu(H,-1); C = Hessprod(A,B); nflps = flops nflps = 1039 Using the multiplication operator * the number of flops used for the same problem is flops(0) C = A*B; nflps = flops nflps = 2000 For more algorithms for computing the matrix-matrix products see the subsequent sections of this tutorial. % "    The goal of this section is to discuss important matrix transformations that are used in numerical linear algebra. On several occasions we will use function ek(k, n) – the kth coordinate vector in the n-dimensional Euclidean space function v = ek(k, n) % The k-th coordinate vector in the n-dimensional Euclidean space. v = zeros(n,1); v(k) = 1; 4.3.1 Gauss transformation In many problems that arise in applied mathematics one wants to transform a matrix to an upper triangular one. This goal can be accomplished using the Gauss transformation (synonym: elementary matrix). Let m, ek  n. The Gauss transformation Mk  M is defined as M = I – mekT. Vector m used here is called the Gauss vector and I is the n-by-n identity matrix. In this section we present two functions for computations with this transformation. For more information about this transformation the reader is referred to [3]. 5 function m = Gaussv(x, k) % Gauss vector m from the vector x and the position % k (k > 0)of the pivot entry. if x(k) == 0 error('Wrong vector') end; n = length(x); x = x(:); if ( k > 0 & k < n ) m = [zeros(k,1);x(k+1:n)/x(k)]; else error('Index k is out of range') end Let M be the Gauss transformation. The matrix-vector product M*b can be computed without forming the matrix M explicitly. Function Gaussprod implements a well-known formula for the product in question. function c = Gaussprod(m, k, b) % Product c = M*b, where M is the Gauss transformation % determined by the Gauss vector m and its column % index k. n = length(b); if ( k < 0 | k > n-1 ) error('Index k is out of range') end b = b(:); c = [b(1:k);-b(k)*m(k+1:n)+b(k+1:n)]; Let x = 1:4; k = 2; m = Gaussv(x,k) m = 0 0 1.5000 2.0000 Then c = Gaussprod(m, k, x) c = 1 2 0 0 6 4.3.2 Householder transformation The Householder transformation H, where H = I – 2uuT, also called the Householder reflector, is a frequently used tool in many problems of numerical linear algebra. Here u stands for the real unit vector. In this section we give several functions for computations with this matrix. function u = Housv(x) % Householder reflection unit vector u from the vector x. m = max(abs(x)); u = x/m; if u(1) == 0 su = 1; else su = sign(u(1)); end u(1) = u(1)+su*norm(u); u = u/norm(u); u = u(:); Let x = [1 2 3 4]'; Then u u = 0.7690 0.2374 0.3561 0.4749 = Housv(x) The Householder reflector H is computed as follows H = eye(length(x))-2*u*u' H = -0.1826 -0.3651 -0.5477 -0.7303 -0.3651 0.8873 -0.1691 -0.2255 -0.5477 -0.1691 0.7463 -0.3382 -0.7303 -0.2255 -0.3382 0.5490 An efficient method of computing the matrix-vector or matrix-matrix products with Householder matrices utilizes a special form of this matrix. 7 function P = Houspre(u, A) % Product P = H*A, where H is the Householder reflector % determined by the vector u and A is a matrix. [n, p] = size(A); m = length(u); if m ~= n error('Dimensions of u and A must agree') end v = u/norm(u); v = v(:); P = []; for j=1:p aj = A(:,j); P = [P aj-2*v*(v'*aj)]; end Let A = pascal(4); and let u = Housv(A(:,1)) u = 0.8660 0.2887 0.2887 0.2887 Then P = Houspre(u, A) P = -2.0000 -0.0000 -0.0000 -0.0000 -5.0000 -0.0000 1.0000 2.0000 -10.0000 -0.6667 2.3333 6.3333 -17.5000 -2.1667 3.8333 13.8333 In some problems that arise in numerical linear algebra one has to compute a product of several Householder transformations. Let the Householder transformations are represented by their normalized reflection vectors stored in columns of the matrix V. The product in question, denoted by Q, is defined as Q = V(:, 1)*V(:, 2)* … *V(:, n) where n stands for the number of columns of the matrix V. 8 function Q = Housprod(V) % Product Q of several Householder transformations % represented by their reflection vectors that are % saved in columns of the matrix V. [m, n] = size(V); Q = eye(m)-2*V(:,n)*V(:,n)'; for i=n-1:-1:1 Q = Houspre(V(:,i),Q); end Among numerous applications of the Householder transformation the following one: reduction of a square matrix to the upper Hessenberg form and reduction of an arbitrary matrix to the upper bidiagonal matrix, are of great importance in numerical linear algebra. It is well known that any square matrix A can always be transformed to an upper Hessenberg matrix H by orthogonal similarity (see [7] for more details). Householder reflectors are used in the course of computations. Function Hessred implements this method function [A, V] = Hessred(A) % % % % % Reduction of the square matrix A to the upper Hessenberg form using Householder reflectors. The reflection vectors are stored in columns of the matrix V. Matrix A is overwritten with its upper Hessenberg form. [m,n] =size(A); if A == triu(A,-1) V = eye(m); return end V = []; for k=1:m-2 x = A(k+1:m,k); v = Housv(x); A(k+1:m,k:m) = A(k+1:m,k:m) - 2*v*(v'*A(k+1:m,k:m)); A(1:m,k+1:m) = A(1:m,k+1:m) - 2*(A(1:m,k+1:m)*v)*v'; v = [zeros(k,1);v]; V = [V v]; end Householder reflectors used in these computations can easily be reconstructed from the columns of the matrix V. Let A = [0 2 3;2 1 2;1 1 1]; To compute the upper Hessenberg form H of the matrix A we run function Hessred to obtain [H, V] = Hessred(A) 9 H = 0 -2.2361 0 V = 0 0.9732 0.2298 -3.1305 2.2000 -0.4000 1.7889 -1.4000 -0.2000 The only Householder reflector P used in the course of computations is shown below P = eye(3)-2*V*V' P = 1.0000 0 0 0 -0.8944 -0.4472 0 -0.4472 0.8944 To verify correctness of these results it suffices to show that P*H*P = A. We have P*H*P ans = 0 2.0000 1.0000 2.0000 1.0000 1.0000 3.0000 2.0000 1.0000 Another application of the Householder transformation is to transform a matrix to an upper bidiagonal form. This reduction is required in some algorithms for computing the singular value decomposition (SVD) of a matrix. Function upbid works with square matrices only function [A, V, U] = upbid(A) % % % % % % % Bidiagonalization of the square matrix A using the Golub- Kahan method. The reflection vectors of the left Householder matrices are saved in columns of the matrix V, while the reflection vectors of the right Householder reflections are saved in columns of the matrix U. Matrix A is overwritten with its upper bidiagonal form. [m, n] = size(A); if m ~= n error('Matrix must be square') end if tril(triu(A),1) == A V = eye(n-1); U = eye(n-2); end V = []; U = []; 10 for k=1:n-1 x = A(k:n,k); v = Housv(x); l = k:n; A(l,l) = A(l,l) - 2*v*(v'*A(l,l)); v = [zeros(k-1,1);v]; V = [V v]; if k < n-1 x = A(k,k+1:n)'; u = Housv(x); p = 1:n; q = k+1:n; A(p,q) = A(p,q) - 2*(A(p,q)*u)*u'; u = [zeros(k,1);u]; U = [U u]; end end Let (see [1], Example 10.9.2, p.579) A = [1 2 3;3 4 5;6 7 8]; Then [B, V, U] = upbid(A) B = -6.7823 0.0000 0.0000 V = 0.7574 0.2920 0.5840 U = 0 -0.9075 -0.4201 0 -0.7248 0.6889 12.7620 1.9741 0.0000 -0.0000 -0.4830 -0.0000 Let the matrices V and U be the same as in the last example and let Q = Housprod(V); P = Housprod(U); Then Q'*A*P ans = -6.7823 0.0000 0.0000 12.7620 1.9741 -0.0000 -0.0000 -0.4830 0.0000 which is the same as the bidiagonal form obtained earlier. 11 4.3.3 Givens transformation Givens transformation (synonym: Givens rotation) is an orthogonal matrix used for zeroing a selected entry of the matrix. See [1] for details. Functions included here deal with this transformation. function J = GivJ(x1, x2) % Givens plane rotation J = [c s;-s c]. Entries c and s % are computed using numbers x1 and x2. if x1 == 0 & x2 == 0 J = eye(2); return end if abs(x2) >= abs(x1) t = x1/x2; s = 1/sqrt(1+t^2); c = s*t; else t = x2/x1; c = 1/sqrt(1+t^2); s = c*t; end J = [c s;-s c]; Premultiplication and postmultiplication by a Givens matrix can be performed without computing a Givens matrix explicitly. function A = preGiv(A, J, i, j) % % % % Premultiplication of A by the Givens rotation which is represented by the 2-by-2 planar rotation J. Integers i and j describe position of the Givens parameters. A([i j],:) = J*A([i j],:); Let A = [1 2 3;-1 3 4;2 5 6]; Our goal is to zeroe the (2,1) entry of the matrix A. First the Givens matrix J is created using function GivJ J = GivJ(A(1,1), A(2,1)) J = -0.7071 -0.7071 0.7071 -0.7071 12 Next, using function preGiv we obtain A = preGiv(A,J,1,2) A = -1.4142 0 2.0000 0.7071 -3.5355 5.0000 0.7071 -4.9497 6.0000 Postmultiplication by the Givens rotation can be accomplished using function postGiv function A = postGiv(A, J, i, j) % % % % Postmultiplication of A by the Givens rotation which is represented by the 2-by-2 planar rotation J. Integers i and j describe position of the Givens parameters. A(:,[i j]) = A(:,[i j])*J; An important application of the Givens transformation is to compute the QR factorization of a matrix. function [Q, A] = Givred(A) % % % % The QR factorization A = Q*R of the rectangular matrix A using Givens rotations. Here Q is the orthogonal matrix. On the output matrix A is overwritten with the matrix R. [m, n] = size(A); if m == n k = n-1; elseif m > n k = n; else k = m-1; end Q = eye(m); for j=1:k for i=j+1:m J = GivJ(A(j,j),A(i,j)); A = preGiv(A,J,j,i); Q = preGiv(Q,J,j,i); end end Q = Q'; Let A = pascal(4) 13 A = 1 1 1 1 1 2 3 4 1 3 6 10 1 4 10 20 Then [Q, R] = Givred(A) Q = 0.5000 0.5000 0.5000 0.5000 R = 2.0000 0.0000 0.0000 -0.0000 5.0000 2.2361 0 0 10.0000 6.7082 1.0000 -0.0000 17.5000 14.0872 3.5000 0.2236 -0.6708 -0.2236 0.2236 0.6708 0.5000 -0.5000 -0.5000 0.5000 -0.2236 0.6708 -0.6708 0.2236 A relative error in the computed QR factorization of the matrix A is norm(A-Q*R)/norm(A) ans = 1.4738e-016  & '    ( A good numerical algorithm for solving a system of linear equations should, among other things, minimize computational complexity. If the matrix of the system has a special structure, then this fact should be utilized in the design of the algorithm. In this section, we give an overview of MATLAB's functions for computing a solution vector x to the linear system Ax = b. To this end, we will assume that the matrix A is a square matrix. 4.4.1 Triangular systems If the matrix of the system is either a lower triangular or upper triangular, then one can easily design a computer code for computing the vector x. We leave this task to the reader (see Problems 2 and 3). 4.4.2 The LU factorization MATLAB's function lu computes the LU factorization PA = LU of the matrix A using a partial pivoting strategy. Matrix L is unit lower triangular, U is upper triangular, and P is the permutation matrix. Since P is orthogonal, the linear system Ax = b is equivalent to LUx =PTb. This method is recommended for solving linear systems with multiple right hand sides. 14 Let A = hilb(5); b = [1 2 3 4 5]'; The following commands are used to compute the LU decomposition of A, the solution vector x, and the upper bound on the relative error in the computed solution [L, U, P] = lu(A); x = U\(L\(P'*b)) x = 1.0e+004 * 0.0125 -0.2880 1.4490 -2.4640 1.3230 rl_err = cond(A)*norm(b-A*x)/norm(b) rl_err = 4.3837e-008 Number of decimal digits of accuracy in the computed solution x is defined as the negative decimal logarithm of the relative error (see e.g., [6]). Vector x of the last example has dda = -log10(rl_err) dda = 7.3582 about seven decimal digits of accuracy. 4.4.3 Cholesky factorization For linear systems with symmetric positive definite matrices the recommended method is based on the Cholesky factorization A = HTH of the matrix A. Here H is the upper triangular matrix with positive diagonal entries. MATLAB's function chol calculates the matrix H from A or generates an error message if A is not positive definite. Once the matrix H is computed, the solution x to Ax = b can be found using the trick used in 4.4.2.     (   ) In some problems of applied mathematics one seeks a solution to the overdetermined linear system Ax = b. In general, such a system is inconsistent. The least squares solution to this system is a vector x that minimizes the Euclidean norm of the residual r = b – Ax. Vector x always exists, however it is not necessarily unique. For more details, see e.g., [7], p. 81. In this section we discuss methods for computing the least squares solution. 15 4.5.1 Using MATLAB built-in functions MATLAB's backslash operator \ can be used to find the least squares solution x = A\b. For the rank deficient systems a warning message is generated during the course of computations. A second MATLAB's function that can be used for computing the least squares solution is the pinv command. The solution is computed using the following command x = pinv(A)*b. Here pinv stands for the pseudoinverse matrix. This method however, requires more flops than the backslash method does. For more information about the pseudoinverses, see Section 4.7 of this tutorial. 4.5.2 Normal equations This classical method, which is due to C.F. Gauss, finds a vector x that satisfies the normal equations ATAx = ATb. The method under discussion is adequate when the condition number of A is small. function [x, dist] = lsqne(A, b) % The least-squares solution x to the overdetermined % linear system Ax = b. Matrix A must be of full column % rank. % Input: % A- matrix of the system % b- the right-hand sides % Output: % x- the least-squares solution % dist- Euclidean norm of the residual b - Ax [m, n] = size(A); if (m <= n) error('System is not overdetermined') end if (rank(A) < n) error('Matrix must be of full rank') end H = chol(A'*A); x = H\(H'\(A'*b)); r = b - A*x; dist = norm(r); Throughout the sequel the following matrix A and the vector b will be used to test various methods for solving the least squares problem format long A = [.5 .501;.5 .5011;0 0;0 0]; b = [1;-1;1;-1]; Using the method of normal equations we obtain [x,dist] = lsqne(A,b) 16 x = 1.0e+004 * 2.00420001218025 -2.00000001215472 dist = 1.41421356237310 One can judge a quality of the computed solution by verifying orthogonality of the residual to the column space of the matrix A. We have err = A'*(b - A*x) err = 1.0e-011 * 0.18189894035459 0.24305336410179 4.5.3 Methods based on the QR factorization of a matrix Most numerical methods for finding the least squares solution to the overdetermined linear systems are based on the orthogonal factorization of the matrix A = QR. There are two variants of the QR factorization method: the full and the reduced factorization. In the full version of the QR factorization the matrix Q is an m-by-m orthogonal matrix and R is an m-by-n matrix with an n-by-n upper triangular matrix stored in rows 1 through n and having zeros everywhere else. The reduced factorization computes an m-by-n matrix Q with orthonormal columns and an n-by-n upper triangular matrix R. The QR factorization of A can be obtained using one of the following methods: (i) (ii) (iii) Householder reflectors Givens rotations Modified Gram-Schmidt orthogonalization Householder QR factorization MATLAB function qr computes matrices Q and R using Householder reflectors. The command [Q, R] = qr(A) generates a full form of the QR factorization of A while [Q, R] = qr(A, 0) computes the reduced form. The least squares solution x to Ax = b satisfies the system of equations RTRx = ATb. This follows easily from the fact that the associated residual r = b – Ax is orthogonal to the column space of A. Thus no explicit knowledge of the matrix Q is required. Function mylsq will be used on several occasions to compute a solution to the overdetermined linear system Ax = b with known QR factorization of A function x = mylsq(A, b, R) % The least squares solution x to the overdetermined % linear system Ax = b. Matrix R is such that R = Q'A, % where Q is a matrix whose columns are orthonormal. m = length(b); [n,n] = size(R); 17 if m < n error('System is not overdetermined') end x = R\(R'\(A'*b)); Assume that the matrix A and the vector b are the same as above. Then [Q,R] = qr(A,0); x = mylsq(A,b,R) x = 1.0e+004 * 2.00420000000159 -2.00000000000159 % Reduced QR factorization of A Givens QR factorization Another method of computing the QR factorization of a matrix uses Givens rotations rather than the Householder reflectors. Details of this method are discussed earlier in this tutorial. This method, however, requires more flops than the previous one. We will run function Givred on the overdetermined system introduced earlier in this chapter [Q,R]= Givred(A); x = mylsq(A,b,R) x = 1.0e+004 * 2.00420000000026 -2.00000000000026 Modified Gram-Schmidt orthogonalization The third method is a variant of the classical Gram-Schmidt orthogonalization. A version used in the function mgs is described in detail in [4]. Mathematically the Gram-Schmidt and the modified Gram-Schmidt method are equivalent, however the latter is more stable. This method requires that matrix A is of a full column rank function [Q, R] = mgs(A) % % % % Modified Gram-Schmidt orthogonalization of the matrix A = Q*R, where Q is orthogonal and R upper is an upper triangular matrix. Matrix A must be of a full column rank. [m, n] = size(A); for i=1:n R(i,i) = norm(A(:,i)); Q(:,i) = A(:,i)/R(i,i); for j=i+1:n 18 R(i,j) = Q(:,i)'*A(:,j); A(:,j) = A(:,j) - R(i,j)*Q(:,i); end end Running function mgs on our test system we obtain [Q,R] = mgs(A); x = mylsq(A,b,R) x = 1.0e+004 * 2.00420000000022 -2.00000000000022 This small size overdetermined linear system was tested using three different functions for computing the QR factorization of the matrix A. In all cases the least squares solution was found using function mylsq. The flop count and the check of orthogonality of Q are contained in the following table. As a measure of closeness of the computed Q to its exact value is determined by errorQ = norm(Q'*Q – eye(k)), where k = 2 for the reduced form and k = 4 for the full form of the QR factorization Function qr(, 0) Givred mgs Flop count 138 488 98 errorQ 2.6803e-016 2.2204e-016 2.2206e-012 For comparison the number of flops used by the backslash operator was equal to 122 while the pinv command found a solution using 236 flops. Another method for computing the least squares solution finds first the QR factorization of the augmented matrix [A b] i.e., QR = [A b] using one of the methods discussed above. The least squares solution x is then found solving a linear system Ux = Qb, where U is an n-by- n principal submatrix of R and Qb is the n+1st column of the matrix R. See e.g., [7] for more details. Function mylsqf implements this method function x = mylsqf(A, b, f, p) % % % % % % % The least squares solution x to the overdetermined linear system Ax = b using the QR factorization. The input parameter f is the string holding the name of a function used to obtain the QR factorization. Fourth input parameter p is optional and should be set up to 0 if the reduced form of the qr function is used to obtain the QR factorization. [m, n] = size(A); if m <= n 19 error('System is not overdetermined') end if nargin == 4 [Q, R] = qr([A b],0); else [Q, R] = feval(f,[A b]); end Qb = R(1:n,n+1); R = R(1:n,1:n); x = R\Qb; A choice of a numerical algorithm for solving a particular problem is often a complex task. Factors that should be considered include numerical stability of a method used and accuracy of the computed solution, to mention the most important ones. It is not our intention to discuss these issues in this tutorial. The interested reader is referred to [5] and [3]. *  & $   "
Many properties of a matrix can be derived from its singular value decomposition (SVD). The SVD is motivated by the following fact: the image of the unit sphere under the m-by-n matrix is a hyperellipse. Function SVDdemo takes a 2-by-2 matrix and generates two graphs: the original circle together with two perpendicular vectors and their images under the transformation used. In the example that follows the function under discussion a unit circle C with center at the origin is transformed using a 2-by-2 matrix A.
function SVDdemo(A) % This illustrates a geometric effect of the application % of the 2-by-2 matrix A to the unit circle C. t = linspace(0,2*pi,200); x = sin(t); y = cos(t); [U,S,V] = svd(A); vx = [0 V(1,1) 0 V(1,2)]; vy = [0 V(2,1) 0 V(2,2)]; axis equal h1_line = plot(x,y,vx,vy); set(h1_line(1),'LineWidth',1.25) set(h1_line(2),'LineWidth',1.25,'Color',[0 0 0]) grid title('Unit circle C and right singular vectors v_i') pause(5) w = [x;y]; z = A*w; U = U*S; udx = [0 U(1,1) 0 U(1,2)]; udy = [0 U(2,1) 0 U(2,2)]; figure h1_line = plot(udx,udy,z(1,:),z(2,:)); set(h1_line(2),'LineWidth',1.25,'Color',[0 0 1]) set(h1_line(1),'LineWidth',1.25,'Color',[0 0 0]) grid

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title('Image A*C of C and vectors \sigma_iu_i')

Define a matrix
A = [1 2;3 4];

Then
SVDdemo(A)

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The full form of the singular value decomposition of the m-by-n matrix A (real or complex) is the factorization of the form A = USV*, where U and V are unitary matrices of dimensions m and n, respectively and S is an m-by-n diagonal matrix with nonnegative diagonal entries stored in the nonincreasing order. Columns of matrices U and V are called the left singular vectors and the right singular vectors, respectively. The diagonal entries of S are the singular values of the matrix A. MATLAB's function svd computes matrices of the SVD of A by invoking the command [U, S, V] = svd(A). The reduced form of the SVD of the matrix A is computed using function svd with a second input parameter being set to zero [U, S, V] = svd(A, 0). If m > n, then only the first n columns of U are computed and S is an n-by-n matrix. Computation of the SVD of a matrix is a nontrivial task. A common method used nowadays is the two-phase method. Phase one reduces a given matrix A to an upper bidiagonal form using the Golub-Kahan method. Phase two computes the SVD of A using a variant of the QR factorization. Function mysvd implements a method proposed in Problem 4.15 in [4]. This code works for the 2-by-2 real matrices only.
function [U, S, V] = mysvd(A) % % % % % % Singular value decomposition A = U*S*V'of a 2-by-2 real matrix A. Matrices U and V are orthogonal. The left and the right singular vectors of A are stored in columns of matrices U and V,respectively. Singular values of A are stored, in the nonincreasing order, on the main diagonal of the diagonal matrix S.

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if A == zeros(2) S = zeros(2); U = eye(2); V = eye(2); return end [S, G] = symmat(A); [S, J] = diagmat(S); U = G'*J; V = J; d = diag(S); s = sign(d); for j=1:2 if s(j) < 0 U(:,j) = -U(:,j); end end d = abs(d); S = diag(d); if d(1) < d(2) d = flipud(d); S = diag(d); U = fliplr(U); V = fliplr(V); end

In order to run this function two other functions symmat and diagmat must be in MATLAB's search path
function [S, G] = symmat(A) % Symmetric 2-by-2 matrix S from the matrix A. Matrices % A, S, and G satisfy the equation G*A = S, where G % is the Givens plane rotation. if A(1,2) == A(2,1) S = A; G = eye(2); return end t = (A(1,1) + A(2,2))/(A(1,2) - A(2,1)); s = 1/sqrt(1 + t^2); c = -t*s; G(1,1) = c; G(2,2) = c; G(1,2)= s; G(2,1) = -s; S = G*A;

function [D, G] = diagmat(A); % % % % Diagonal matrix D obtained by an application of the two-sided Givens rotation to the matrix A. Second output parameter G is the Givens rotation used to diagonalize matrix A, i.e., G.'*A*G = D.

23

if A ~= A' error('Matrix must be symmetric') end if abs(A(1,2)) < eps & abs(A(2,1)) < eps D = A; G = eye(2); return end r = roots([-1 (A(1,1)-A(2,2))/A(1,2) 1]); [t, k] = min(abs(r)); t = r(k); c = 1/sqrt(1+t^2); s = c*t; G = zeros(size(A)); G(1,1) = c; G(2,2) = c; G(1,2) = s; G(2,1) = -s; D = G.'*A*G;

Let
A = [1 2;3 4];

Then
[U,S,V] = mysvd(A) U = 0.4046 0.9145 S = 5.4650 0 V = 0.5760 0.8174 0.8174 -0.5760 0 0.3660 -0.9145 0.4046

To verify this result we compute
AC = U*S*V' AC = 1.0000 3.0000 2.0000 4.0000

and the relative error in the computed SVD decomposition
norm(AC-A)/norm(A) ans = 1.8594e-016

24

Another algorithm for computing the least squares solution x of the overdetermined linear system Ax = b utilizes the singular value decomposition of A. Function lsqsvd should be used for illconditioned or rank deficient matrices.
function x = lsqsvd(A, b) % The least squares solution x to the overdetermined % linear system Ax = b using the reduced singular % value decomposition of A. [m, n] = size(A); if m <= n error('System must be overdetermined') end [U,S,V] = svd(A,0); d = diag(S); r = sum(d > 0); b1 = U(:,1:r)'*b; w = d(1:r).\b1; x = V(:,1:r)*w; re = b - A*x; % One step of the iterative b1 = U(:,1:r)'*re; % refinement w = d(1:r).\b1; e = V(:,1:r)*w; x = x + e;

The linear system with
A = ones(6,3); b = ones(6,1);

is ill-conditioned and rank deficient. Therefore the least squares solution to this system is not unique
x = lsqsvd(A,b) x = 0.3333 0.3333 0.3333

   
    
Another application of the SVD is for computing the pseudoinverse of a matrix. Singular or rectangular matrices always possess the pseudoinverse matrix. Let the matrix A be defined as follows
A = [1 2 3;4 5 6] A = 1 4 2 5 3 6

25

Its pseudoinverse is
B = pinv(A) B = -0.9444 -0.1111 0.7222 0.4444 0.1111 -0.2222

The pseudoinverse B of the matrix A satisfy the Penrose conditions ABA = A, BAB = B, (AB)T = AB, (BA)T = BA We will verify the first condition only
norm(A*B*A-A) ans = 3.6621e-015

and leave it to the reader to verify the remaining ones.

+  " & $ The matrix eigenvalue problem, briefly discussed in Tutorial 3, is one of the central problems in the numerical linear algebra. It is formulated as follows. Given a square matrix A = [aij], 1 i, j  n, find a nonzero vector x  n and a number  that satisfy the equation Ax = x. Number  is called the eigenvalue of the matrix A and x is the associated right eigenvector of A. In this section we will show how to localize the eigenvalues of a matrix using celebrated Gershgorin's Theorem. Also, we will present MATLAB's code for computing the dominant eigenvalue and the associated eigenvector of a matrix. The QR iteration for computing all eigenvalues of the symmetric matrices is also discussed. Gershgorin Theorem states that each eigenvalue  of the matrix A satisfies at least one of the following inequalities | - akk|  rk, where rk is the sum of all off-diagonal entries in row k of the matrix |A| (see, e.g., [1], pp.400-403 for more details). Function Gershg computes the centers and the radii of the Gershgorin circles of the matrix A and plots all Gershgorin circles. The eigenvalues of the matrix A are also displayed. function [C] = Gershg(A) % Gershgorin's circles C of the matrix A. d = diag(A); cx = real(d); cy = imag(d); B = A - diag(d); 26 [m, n] = size(A); r = sum(abs(B')); C = [cx cy r(:)]; t = 0:pi/100:2*pi; c = cos(t); s = sin(t); [v,d] = eig(A); d = diag(d); u1 = real(d); v1 = imag(d); hold on grid on axis equal xlabel('Re') ylabel('Im') h1_line = plot(u1,v1,'or'); set(h1_line,'LineWidth',1.5) for i=1:n x = zeros(1,length(t)); y = zeros(1,length(t)); x = cx(i) + r(i)*c; y = cy(i) + r(i)*s; h2_line = plot(x,y); set(h2_line,'LineWidth',1.2) end hold off title('Gershgorin circles and the eigenvalues of a') To illustrate functionality of this function we define a matrix A, where A = [1 2 3;3 4 9;1 1 1]; Then C = Gershg(A) C = 1 4 1 0 0 0 5 12 2 27 Gershgorin circles and the eigenvalues of a matrix 10 5 Im 0 -5 -10 -10 -5 0 Re 5 10 15 Information about each circle (coordinates of the origin and its radius) is contained in successive rows of the matrix C. It is well known that the eigenvalues are sensitive to small changes in the entries of the matrix (see, e.g., [3]). The condition number of the simple eigenvalue  of the matrix A is defined as follows Cond() = 1/|yTx| where y and x are the left and right eigenvectors of A, respectively with ||x||2 = ||y||2 = 1. Recall that a nonzero vector y is said to be a left eigenvector of A if yTA = yT. Clearly Cond()  1. Function eigsen computes the condition number of all eigenvalues of a matrix. function s = eigsen(A) % Condition numbers s of all eigenvalues of the diagonalizable % matrix A. [n,n] = size(A); [v1,la1] = eig(A); [v2,la2] = eig(A'); [d1, j] = sort(diag(la1)); v1 = v1(:,j); [d2, j] = sort(diag(la2)); v2 = v2(:,j); s = []; for i=1:n v1(:,i) = v1(:,i)/norm(v1(:,i)); v2(:,i) = v2(:,i)/norm(v2(:,i)); s = [s;1/abs(v1(:,i)'*v2(:,i))]; end 28 In this example we will illustrate sensitivity of the eigenvalues of the celebrated Wilkinson's matrix W. Its is an upper bidiagonal 20-by-20 matrix with diagonal entries 20, 19, … , 1. The superdiagonal entries are all equal to 20. We create this matrix using some MATLAB functions that are discussed in Section 4.9. W = spdiags([(20:-1:1)', 20*ones(20,1)],[0 1], 20,20); format long s = eigsen(full(W)) s = 1.0e+012 * 0.00008448192546 0.00145503286853 0.01206523295175 0.06389158525507 0.24182386727359 0.69411856608888 1.56521713930244 2.83519277292867 4.18391920177580 5.07256664475500 5.07256664475500 4.18391920177580 2.83519277292867 1.56521713930244 0.69411856608888 0.24182386727359 0.06389158525507 0.01206523295175 0.00145503286853 0.00008448192546 Clearly all eigenvalues of the Wilkinson's matrix are sensitive. Let us perturb the w20,1 entry of W W(20,1)=1e-5; and next compute the eigenvalues of the perturbed matrix eig(full(W)) ans = -1.00978219090288 -0.39041284468158 -0.39041284468158 1.32106082150033 1.32106082150033 3.88187526711025 3.88187526711025 7.03697639135041 + + + + 2.37019976472684i 2.37019976472684i 4.60070993953446i 4.60070993953446i 6.43013503466255i 6.43013503466255i 7.62654906220393i 29 7.03697639135041 10.49999999999714 10.49999999999714 13.96302360864989 13.96302360864989 17.11812473289285 17.11812473289285 19.67893917849915 19.67893917849915 21.39041284468168 21.39041284468168 22.00978219090265 + + + + + - 7.62654906220393i 8.04218886506797i 8.04218886506797i 7.62654906220876i 7.62654906220876i 6.43013503466238i 6.43013503466238i 4.60070993953305i 4.60070993953305i 2.37019976472726i 2.37019976472726i Note a dramatic change in the eigenvalues. In some problems only selected eigenvalues and associated eigenvectors are needed. Let the eigenvalues {k } be rearranged so that |1| > |2|  …  |n|. The dominant eigenvalue 1 and/or the associated eigenvector can be found using one of the following methods: power iteration, inverse iteration, and Rayleigh quotient iteration. Functions powerit and Rqi implement the first and the third method, respectively. function [la, v] = powerit(A, v) % % % % % Power iteration with the Rayleigh quotient. Vector v is the initial estimate of the eigenvector of the matrix A. Computed eigenvalue la and the associated eigenvector v satisfy the inequality% norm(A*v - la*v,1) < tol, where tol = length(v)*norm(A,1)*eps. if norm(v) ~= 1 v = v/norm(v); end la = v'*A*v; tol = length(v)*norm(A,1)*eps; while norm(A*v - la*v,1) >= tol w = A*v; v = w/norm(w); la = v'*A*v; end function [la, v] = Rqi(A, v, iter) % % % % % % The Rayleigh quotient iteration. Vector v is an approximation of the eigenvector associated with the dominant eigenvalue la of the matrix A. Iterative process is terminated either if norm(A*v - la*v,1) < norm(A,1)*length(v)*eps or if the number of performed iterations reaches the allowed number of iterations iter. if norm(v) > 1 v = v/norm(v); end la = v'*A*v; tol = norm(A,1)*length(v)*eps; for k=1:iter 30 if norm(A*v - la*v,1) < tol return else w = (A - la*eye(size(A)))\v; v = w/norm(w); la = v'*A*v; end end Let ( [7], p.208, Example 27.1) A = [2 1 1;1 3 1;1 1 4]; v = ones(3,1); Then format long flops(0) [la, v] = powerit(A, v) la = 5.21431974337753 v = 0.39711254978701 0.52065736843959 0.75578934068378 flops ans = 3731 Using function Rqi, for computing the dominant eigenpair of the matrix A, we obtain flops(0) [la, v] = Rqi(A,ones(3,1),5) la = 5.21431974337754 v = 0.39711254978701 0.52065736843959 0.75578934068378 flops ans = 512 31 Once the dominant eigenvalue (eigenpair) is computed one can find another eigenvalue or eigenpair by applying a process called deflation. For details the reader is referred to [4], pp. 127-128. function [l2, v2, B] = defl(A, v1) % % % % Deflated matrix B from the matrix A with a known eigenvector v1 of A. The eigenpair (l2, v2) of the matrix A is computed. Functions Housv, Houspre, Housmvp and Rqi are used in the body of the function defl. n = length(v1); v1 = Housv(v1); C = Houspre(v1,A); B = []; for i=1:n B = [B Housmvp(v1,C(i,:))]; end l1 = B(1,1); b = B(1,2:n); B = B(2:n,2:n); [l2, y] = Rqi(B, ones(n-1,1),10); if l1 ~= l2 a = b*y/(l2-l1); v2 = Housmvp(v1,[a;y]); else v2 = v1; end Let A be an 5-by-5 Pei matrix, i.e., A = ones(5)+diag(ones(5,1)) A = 2 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 2 Its dominant eigenvalue is 1 = 6 and all the remaining eigenvalues are equal to one. To compute the dominant eigenpair of A we use function Rqi [l1,v1] = Rqi(A,rand(5,1),10) l1 = 6.00000000000000 v1 = 0.44721359549996 0.44721359549996 0.44721359549996 0.44721359549996 0.44721359549996 and next apply function defl to compute another eigenpair of A 32 [l2,v2] = defl(A,v1) l2 = 1.00000000000000 v2 = -0.89442719099992 0.22360679774998 0.22360679774998 0.22360679774998 0.22360679774998 To check these results we compute the norms of the "residuals" [norm(A*v1-l1*v1);norm(A*v2-l2*v2)] ans = 1.0e-014 * 0.07691850745534 0.14571016336181 To this end we will deal with the symmetric eigenvalue problem. It is well known that the eigenvalues of a symmetric matrix are all real. One of the most efficient algorithms is the QR iteration with or without shifts. The algorithm included here is the two-phase algorithm. Phase one reduces a symmetric matrix A to the symmetric tridiagonal matrix T using MATLAB's function hess. Since T is orthogonally similar to A, sp(A) = sp(T). Here sp stands for the spectrum of a matrix. During the phase two the off diagonal entries of T are annihilated. This is an iterative process, which theoretically is an infinite one. In practice, however, the off diagonal entries approach zero fast. For details the reader is referred to [2] and [7]. Function qrsft computes all eigenvalues of the symmetric matrix A. Phase two uses Wilkinson's shift. The latter is computed using function wsft. function [la, v] = qrsft(A) % All eigenvalues la of the symmetric matrix A. % Method used: the QR algorithm with Wilkinson's shift. % Function wsft is used in the body of the function qrsft. [n, n] = size(A); A = hess(A); la = []; i = 0; while i < n [j, j] = size(A); if j == 1 la = [la;A(1,1)]; return end mu = wsft(A); [Q, R] = qr(A - mu*eye(j)); A = R*Q + mu*eye(j); 33 if abs(A(j,j-1))< 10*(abs(A(j-1,j-1))+abs(A(j,j)))*eps la = [la;A(j,j)]; A = A(1:j-1,1:j-1); i = i + 1; end end function mu = wsft(A) % Wilkinson's shift mu of the symmetric matrix A. [n, n] = size(A); if A == diag(diag(A)) mu = A(n,n); return end mu = A(n,n); if n > 1 d = (A(n-1,n-1)-mu)/2; if d ~= 0 sn = sign(d); else sn = 1; end bn = A(n,n-1); mu = mu - sn*bn^2/(abs(d) + sqrt(d^2+bn^2)); end We will test function qrsft on the matrix A used earlier in this section A = [2 1 1;1 3 1;1 1 4]; la = qrsft(A) la = 5.21431974337753 2.46081112718911 1.32486912943335 Function eigv computes both the eigenvalues and the eigenvectors of a symmetric matrix provided the eigenvalues are distinct. A method for computing the eigenvectors is discussed in [1], Algorithm 8.10.2, pp. 452-454 function [la, V] = eigv(A) % Eigenvalues la and eigenvectors V of the symmetric % matrix A with distinct eigenvalues. V = []; [n, n] = size(A); [Q,T] = schur(A); la = diag(T); 34 if nargout == 2 d = diff(sort(la)); for k=1:n-1 if d(k) < 10*eps d(k) = 0; end end if ~all(d) disp('Eigenvalues must be distinct') else for k=1:n U = T - la(k)*eye(n); t = U(1:k,1:k); y1 = []; if k>1 t11 = t(1:k-1,1:k-1); s = t(1:k-1,k); y1 = -t11\s; end y = [y1;1]; z = zeros(n-k,1); y = [y;z]; v = Q*y; V = [V v/norm(v)]; end end end We will use this function to compute the eigenvalues and the eigenvectors of the matrix A of the last example [la, V] = eigv(A) la = 1.32486912943335 2.46081112718911 5.21431974337753 V = 0.88765033882045 -0.42713228706575 -0.17214785894088 -0.23319197840751 -0.73923873953922 0.63178128111780 0.39711254978701 0.52065736843959 0.75578934068378 To check these results let us compute the residuals Av - v A*V-V*diag(la) ans = 1.0e-014 * 0 -0.02220446049250 0 -0.09992007221626 -0.42188474935756 0.11102230246252 0.13322676295502 0.44408920985006 -0.13322676295502 35 , $   
MATLAB has several built-in functions for computations with sparse matrices. A partial list of these functions is included here.

Function condest eigs find full issparse nnz nonzeros sparse spdiags speye spfun sprand sprandsym spy svds

Description Condition estimate for sparse matrix Few eigenvalues Find indices of nonzero entries Convert sparse matrix to full matrix True for sparse matrix Number of nonzero entries Nonzero matrix entries Create sparse matrix Sparse matrix formed from diagonals Sparse identity matrix Apply function to nonzero entries Sparse random matrix Sparse random symmetric matrix Visualize sparsity pattern Few singular values

Function spy works for matrices in full form as well.

Computations with sparse matrices
The following MATLAB functions work with sparse matrices: chol, det, inv, jordan, lu, qr, size, \. Command sparse is used to create a sparse form of a matrix. Let
A = [0 0 1 1; 0 1 0 0; 0 0 0 1];

Then
B = sparse(A) B = (2,2) (1,3) (1,4) (3,4) 1 1 1 1

Command full converts a sparse form of a matrix to the full form

36

C = full(B) C = 0 0 0 0 1 0 1 0 0 1 0 1

Command sparse has the following syntax
sparse(k,l,s,m,n)

where k and l are arrays of row and column indices, respectively, s ia an array of nonzero numbers whose indices are specified in k and l, and m and n are the row and column dimensions, respectively. Let
S = sparse([1 3 5 2], [2 1 3 4], [1 2 3 4], 5, 5) S = (3,1) (1,2) (5,3) (2,4) F = full(S) F = 0 0 2 0 0 1 0 0 0 0 0 0 0 0 3 0 4 0 0 0 0 0 0 0 0 2 1 3 4

To create a sparse matrix with several diagonals parallel to the main diagonal one can use the command spdiags. Its syntax is shown below
spdiags(B, d, m, n)

The resulting matrix is an m-by-n sparse matrix. Its diagonals are the columns of the matrix B. Location of the diagonals are described in the vector d. Function mytrid creates a sparse form of the tridiagonal matrix with constant entries along the diagonals.
function T = mytrid(a,b,c,n) % % % % The n-by-n tridiagonal matrix T with constant entries along diagonals. All entries on the subdiagonal, main diagonal,and the superdiagonal are equal a, b, and c, respectively.

37

e = ones(n,1); T = spdiags([a*e b*e c*e],-1:1,n,n);

To create a symmetric 6-by-6-tridiagonal matrix with all diagonal entries are equal 4 and all subdiagonal and superdiagonal entries are equal to one execute the following command
T = mytrid(1,4,1,6);

Function spy creates a graph of the matrix. The nonzero entries are displayed as the dots.
spy( T )

0

1

2

3

4

5

6

7

0

1

2

3 4 nz = 16

5

6

7

The following is the example of a sparse matrix created with the aid of the nonsparse matrix magic
spy(rem(magic(16),2))

38

0 2 4 6 8 10 12 14 16 0 5 10 nz = 128 15

Using a sparse form rather than the full form of a matrix one can reduce a number of flops used. Let
A = sprand(50,50,.25);

The above command generates a 50-by-50 random sparse matrix A with a density of about 25%. We will use this matrix to solve a linear system Ax = b with
b = ones(50,1);

Number of flops used is determined in usual way
flops(0) A\b; flops ans = 54757

39

Using the full form of A the number of flops needed to solve the same linear system is
flops(0) full(A)\b; flops ans = 72014

40

-
[1] B.N. Datta, Numerical Linear Algebra and Applications, Brooks/Cole Publishing Company, Pacific Grove, CA, 1995. [2] J.W. Demmel, Applied Numerical Linear Algebra, SIAM, Philadelphia, PA, 1997. [3] G.H. Golub and Ch.F. Van Loan, Matrix Computations, Second edition, Johns Hopkins University Press, Baltimore, MD, 1989. [4] M.T. Heath, Scientific Computing: An Introductory Survey, McGraw-Hill, Boston, MA, 1997. [5] N.J. Higham, Accuracy and Stability of Numerical Algorithms, SIAM, Philadelphia, PA, 1996. [6] R.D. Skeel and J.B. Keiper, Elementary Numerical Computing with Mathematica, McGraw-Hill, New York, NY, 1993. [7] L.N. Trefethen and D. Bau III, Numerical Linear Algebra, SIAM, Philadelphia, PA, 1997.

41

.
1. Let A by an n-by-n matrix and let v be an n-dimensional vector. Which of the following methods is faster? (i) (ii) (v*v')*A v*(v'*A)

2. Suppose that L  n x n is lower triangular and b  n. Write MATLAB function x = ltri(L, b) that computes a solution x to the linear system Lx = b. 3. Repeat Problem 2 with L being replaced by the upper triangular matrix U. Name your function utri(U, b). 4. Let A  n x n be a triangular matrix. Write a function dettri(A) that computes the determinant of the matrix A. 5. Write MATLAB function MA = Gausspre(A, m, k) that overwrites matrix A  n x p with the product MA, where M  n x n is the Gauss transformation which is determined by the Gauss vector m and its column index k. Hint: You may wish to use the following formula MA = A – m(ekTA). 6. A system of linear equations Ax = b, where A is a square matrix, can be solved applying successively Gauss transformations to the augmented matrix [A, b]. A solution x then can be found using back substitution, i.e., solving a linear system with an upper triangular matrix. Using functions Gausspre of Problem 5, Gaussv described in Section 4.3, and utri of Problem 3, write a function x = sol(A, b) which computes a solution x to the linear system Ax = b. 7. Add a few lines of code to the function sol of Problem 6 to compute the determinant of the matrix A. The header of your function might look like this function [x, d] = sol(A, b). The second output parameter d stands for the determinant of A. 8. The purpose of this problem is to test function sol of Problem 6.

(i) (ii)

Construct at least one matrix A for which function sol fails to compute a solution. Explain why it happened. Construct at least one matrix A for which the computed solution x is poor. For comparison of a solution you found using function sol with an acceptable solution you may wish to use MATLAB's backslash operator \. Compute the relative error in x. Compare numbers of flops used by function sol and MATLAB's command \. Which of these methods is faster in general?

9. Given a square matrix A. Write MATLAB function [L, U] = mylu(A) that computes the LU decomposition of A using partial pivoting.

42

10. Change your working format to format long e and run function mylu of Problem 11on the following matrices (i) (ii) (iii) (iv) A = [eps 1; 1 1] A = [1 1; eps 1] A = hilb(10) A = rand(10)

In each case compute the error A - LUF.
11. Let A be a tridiagonal matrix that is either diagonally dominant or positive definite. Write MATLAB's function [L, U] = trilu(a, b, c) that computes the LU factorization of A. Here a, b, and c stand for the subdiagonal, main diagonal, and superdiagonal of A, respectively. 12. The following function computes the Cholesky factor L of the symmetric positive definite matrix A. Matrix L is lower triangular and satisfies the equation A = LLT.
function L = mychol(A) % Cholesky factor L of the matrix A; A = L*L'. [n, n] = size(A); for j=1:n for k=1:j-1 A(j:n,j) = A(j:n,j) - A(j:n,k)*A(j,k); end A(j,j) = sqrt(A(j,j)); A(j+1:n,j) = A(j+1:n,j)/A(j,j); end L = tril(A);

Add a few lines of code that generates the error messages when A is neither • • symmetric nor positive definite

Test the modified function mychol on the following matrices (i) (ii) A = [1 2 3; 2 1 4; 3 4 1] A = rand(5)

13. Prove that any 2-by-2 Householder reflector is of the form H = [cos  sin ; sin  -cos ]. What is the Householder reflection vector u of H? 14. Find the eigenvalues and the associated eigenvectors of the matrix H of Problem 13. 15. Write MATLAB function [Q, R] = myqr(A) that computes a full QR factorization A = QR of A  m x n with m  n using Householder reflectors. The output matrix Q is an mby-m orthogonal matrix and R is an m-by-n upper triangular with zero entries in rows n+1 through m.

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Hint: You may wish to use function Housprod in the body of the function myqr. 16. Let A be an n-by-3 random matrix generated by the MATLAB function rand. In this exercise you are to plot the error A - QRF versus n for n = 3, 5, … , 25. To compute the QR factorization of A use the function myqr of Problem 15. Plot the graph of the computed errors using MATLAB's function semilogy instead of the function plot. Repeat this experiment several times. Does the error increase as n does? 17. Write MATLAB function V = Vandm(t, n) that generates Vandermonde's matrix V used in the polynomial least-squares fit. The degree of the approximating polynomial is n while the x-coordinates of the points to be fitted are stored in the vector t. 18. In this exercise you are to compute coefficients of the least squares polynomials using four methods, namely the normal equations, the QR factorization, modified Gram-Schmidt orthogonalization and the singular value decomposition. Write MATLAB function C = lspol(t, y, n) that computes coefficients of the approximating polynomials. They should be saved in columns of the matrix C  (n+1) x 4. Here n stands for the degree of the polynomial, t and y are the vectors holding the x- and the y-coordinates of the points to be approximated, respectively. Test your function using t = linspace(1.4, 1.8), y = sin(tan(t)) – tan(sin(t)), n = 2, 4, 8. Use format long to display the output to the screen. Hint: To create the Vandermonde matrix needed in the body of the function lspol you may wish to use function Vandm of Problem 17. 19. Modify function lspol of Problem 18 adding a second output parameter err so that the header of the modified function should look like this function [C, err] = lspol(t, y, n). Parameter err is the least squares error in the computed solution c to the overdetermined linear system Vc  y. Run the modified function on the data of Problem 18. Which of the methods used seems to produce the least reliable numerical results? Justify your answer. 20. Write MATLAB function [r, c] = nrceig(A) that computes the number of real and complex eigenvalues of the real matrix A. You cannot use MATLAB function eig. Run function nrceig on several random matrices generated by the functions rand and randn. Hint: You may wish to use the following MATLAB functions schur, diag, find. Note that the diag function takes a second optional argument. 21. Assume that an eigenvalue of a matrix is sensitive if its condition number is greater than 103. Construct an n-by-n matrix (5  n  10) whose all eigenvalues are real and sensitive. 22. Write MATLAB function A = pent(a, b, c, d, e, n) that creates the full form of the n-by-n pentadiagonal matrix A with constant entries a along the second subdiagonal, constant entries b along the subdiagonal, etc. 23. Let A = pent(1, 26, 66, 26, 1, n) be an n-by-n symmetric pentadiagonal matrix generated by function pent of Problem 22. Find the eigenvalue decomposition A = QQT of A for various values of n. Repeat this experiment using random numbers in the band of the matrix A. Based on your observations, what conjecture can be formulated about the eigenvectors of A? 24. Write MATLAB function [la, x] = smeig(A, v) that computes the smallest

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(in magnitude) eigenvalue of the nonsingular matrix A and the associated eigenvector x. The input parameter v is an estimate of the eigenvector of A that is associated with the largest (in magnitude) eigenvalue of A. 25. In this exercise you are to experiment with the eigenvalues and eigenvectors of the partitioned matrices. Begin with a square matrix A with known eigenvalues and eigenvectors. Next construct a matrix B using MATLAB's built-in function repmat to define the matrix B as B = repmat(A, 2, 2). Solve the matrix eigenvalue problem for the matrix B and compare the eigenvalues and eigenvectors of matrices A and B. You may wish to continue in this manner using larger values for the second and third parameters in the function repmat. Based on the results of your experiment, what conjecture about the eigenvalues and eigenvectors of B can be formulated?