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Lecture 22 - Exam 2 Review CVEN 302 July 23, 2001 Lecture’s Goals • Chapter 6 - LU Decomposition • Chapter 7 - Eigen-analysis • Chapter 8 - Interpolation • Chapter 9 - Approximation • Chapter 11 - Numerical Differentiation and Integration Chapter 6 LU Decomposition of Matrices LU Decomposition • A modification of the elimination method, called the LU decomposition. The technique will rewrite the matrix as the product of two matrices. A = LU LU Decomposition There are variation of the technique using different methods. – Crout’s reduction (U has ones on the diagonal) – Doolittle’s method( L has ones on the diagonal) – Cholesky’s method ( The diagonal terms are the same value for the L and U matrices) LU Decomposition Solving Using the LU decomposition [A]{x} = [L][U]{x} = [L]{[U]{x}} = {b} Solve [L]{y} = {b} and then solve [U]{x} = {y} LU Decomposition The matrices are represented by LU Decomposition (Crout’s reduction) • Matrix decomposition LU Decomposition (Doolittle’s method) • Matrix decomposition Cholesky’s method • Matrix is decomposed into: • where, lii = uii Tridiagonal Matrix For a banded matrix using Doolittle’s method, i.e. a tridiagonal matrix. 1 0 0 0 u11 u12 0 0 a11 a12 0 0 l 0 0 u 22 0 a 21 a 22 0 21 1 0 u 23 a 23 0 l32 1 0 0 0 u 33 u 34 0 a 32 a 33 a 34 0 0 l 43 1 0 0 0 u 44 0 0 a 43 a 44 Pivoting of the LU Decomposition • Still need pivoting in LU decomposition • Messes up order of [L] • What to do? • Need to pivot both [L] and a permutation matrix [P] • Initialize [P] as identity matrix and pivot when [A] is pivoted Also pivot [L] Pivoting of the LU Decomposition • Permutation matrix [ P ] - permutation of identity matrix [ I ] • Permutation matrix performs “bookkeeping” associated with the row exchanges • Permuted matrix [ P ] [ A ] • LU factorization of the permuted matrix [P][A]=[L][U] Chapter 7 Eigen-Analysis Eigen-Analysis • Matrix eigenvalues arise from discrete models of physical systems • Discrete models – Finite number of degrees of freedom result in a finite number of eigenvalues and eigenvectors. Eigenvalues Computing eigenvalues of a matrix is important in numerous applications – In numerical analysis, the convergence of an iterative sequence involving matrices is determined by the size of the eigenvalues of the iterative matrix. – In dynamic systems, the eigenvalues indicate whether a system is oscillatory, stable (decaying oscillations) or unstable(growing oscillation) – Oscillator system, the eigenvalues of differential equations or the coefficient matrix of a finite element model are directly related to natural frequencies of the system – Regression analysis, eigenvectors of correlation matrix are used to select new predictor variables that are linear combinations of the original predictor variables. General form of the equations • The general form of the equations Ax x Ax I x 0 A I x 0 A I 0 Power Method The basic computation of the power method is summarized as Auk -1 uk and lim uk 1 Auk -1 k The equation can be written as: Auk -1 Auk -1 1uk -1 1 uk -1 Power Method The basic computation of the power method is summarized as Auk -1 uk and lim uk 1 Auk -1 k The equation can be written as: Auk -1 Auk -1 1uk -1 1 uk -1 Shift method It is possible to obtain another eigenvalue from the set equations by using a technique known as shifting the matrix. Ax x Subtract the a vector from each side, thereby changing the maximum eigenvalue Ax sI x s x Shift method The eigenvalue, s, is the maximum value of the matrix A. The matrix is rewritten in a form. B A m axI Use the Power method to obtain the largest eigenvalue of [B]. Inverse Power Method The inverse method is similar to the power method, except that it finds the smallest eigenvalue. Using the following technique. Ax x A Ax A 1 1 x 1 x A x 1 x Bx Inverse Power Method The algorithm is the same as the Power method and the “eigenvector” is not the eigenvector for the smallest eigenvalue. To obtain the smallest eigenvalue from the power method. 1 1 Accelerated Power Method The Power method can be accelerated by using the Rayleigh Quotient instead of the largest wk value. Az1 1 The Rayeigh Quotient is defined as: z' w 1 z' z Accelerated Power Method The values of the next z term is defined as: w z2 1 The Power method is adapted to use the new value. QR factorization • Another form of factorization • A = Q*R • Produces an orthogonal matrix (“Q”) and a right upper triangular matrix (“R”) • Orthogonal matrix - inverse is transpose 1 Q Q T QR factorization Why do we care? We can use Q and R to find eigenvalues 1. Get Q and R (A = Q*R) 2. Let A = R*Q 3. Diagonal elements of A are eigenvalue approximations 4. Iterate until converged Note: QR eigenvalue method gives all eigenvalues simultaneously, not just the dominant Householder Matrix • Householder matrix reduces zk+1 ,…,zn to zero v H I 2 ww ; w v2 x x 1 x2 xk x k 1 x n y Hx y 1 y2 yk 0 0 • To achieve the above operation, v must be a linear combination of x and ek ek 0,0,...,0,1,0....,0 T v x e k x 1 , x 2 ,, x k 1 , x k , x k 1 ,, x n Chapter 8 Interpolation Interpolation Methods Interpolation uses the data to approximate a function, which will fit all of the data points. All of the data is used to approximate the values of the function inside the bounds of the data. We will look at polynomial and rational function interpolation of the data and piece-wise interpolation of the data. Polynomial Interpolation Methods • Lagrange Interpolation Polynomial - a straightforward, but computational awkward way to construct an interpolating polynomial. • Newton Interpolation Polynomial - there is no difference between the Newton and Lagrange results. The difference between the two is the approach to obtaining the coefficients. Hermite Interpolation The Advantages • The segments of the piecewise Hermite polynomial have a continuous first derivative at support points. • The shape of the function being interpolated is better matched, because the tangent of this function and tangent of Hermite polynomial agree at the support points Rational Function Interpolation Polynomial are not always the best match of data. A rational function can be used to represent the steps. A rational function is a ratio of two polynomials. This is useful when you deal with fitting imaginary functions z=x + iy. The Bulirsch- Stoer algorithm creates a function where the numerator is of the same order as the denominator or 1 less. Rational Function Interpolation The Rational Function interpolation are required for the location and function value need to be known. x 3 a1 x 2 b1 x c1 Pi x 3 x a2 x b2 x c2 2 or x 2 b1 x c1 Pj x 3 x a2 x 2 b2 x c2 Cubic Spline Interpolation Hermite Polynomials produce a smooth interpolation, they have a disadvantage that the slope of the input function must be specified at each breakpoint. Cubic Splines interpolation use only the data points used to maintaining the desired smoothness of the function and is piecewise continuous. Chapter 9 Approximation Approximation Methods What is the difference between approximation and interpolation? • Interpolation matches the data points exactly. In case of experimental data, this assumption is not often true. • Approximation - we want to consider the curve that will fit the data with the smallest “error”. Least Square Fit Approximations The solution is the minimization of the sum of squares. This will give a least square solution. S ek 2 This is known as the maximum likelihood principle. Least Square Error How do you minimize the error? dS 0 Take the derivative with da the coefficients and set it dS equal to zero. 0 db Least Square Coefficients for Quadratic fit The equations can be written as: N 4 N N N 2 xi xi3 xi2 xi Yi iN1 i 1 i 1 a i 1 b x Y N N N x3 i xi i i xi2 N i 1 i 1 i 1 c i 1 N N xi 2 x N Yi i 1 i 1 i i 1 Polynomial Least Square The technique can be used to all forms of polynomials of the form: y a0 a1 x a2 x 2 an x n N N N N x i x a Yi n i N i 1 i 1 0 N1 i x a1 x Y i i i i 1 i 1 N a n N 2n N xin xi xi Yi n i 1 i 1 i 1 Polynomial Least Square Solving large sets of linear equations are not a simple task. They can have the undesirable property known as ill-conditioning. The results of this method is that round-off errors in solving for the coefficients cause unusually large errors in the curve fits. Polynomial Least Square Or measure of the variance of the problem N Yk yk 1 2 2 N n 1 k 1 Where, n is the degree polynomial and N is the number of elements and Yk are the data points and, n yk a x j j k j 0 Nonlinear Least Squared Approximation Method How would you handle a problem, which is modeled as: y bx a or y be ax Nonlinear Least Squared Approximation Method Take the natural log of the equations y bx ln y ln b a ln x a y b a x and y be ln y ln b ax ax y b ax Continuous Least Square Functions Instead of modeling a known complex function over a region, we would like to model the values with a simple polynomial. This technique uses a least squares over a continuous region. The coefficients of the polynomial can be determined using same technique that was used in discrete method. Continuous Least Square Functions The technique minimizes the error of the function uses an integral. b E f x sx dx 2 a where f x a0 a1 x a2 x 2 Continuous Least Square Functions Take the derivative of the error with respect to the coefficients and set it equal to zero. b df x 2 f x sx dE da dx 0 dai a i And compute the components of the coefficient matrix. The right hand side of the matrix will be the function we are modeling times a x value. Continuous Least Square Function There are other forms of equations, which can be used to represent continuous functions. Examples of these functions are • Legrendre Polynomials • Tchebyshev Polynomials • Cosines and sines. Legendre Polynomial The Legendre polynomials are a set of orthogonal functions, which can be used to represent a function as components of a function. f x a0 P0 x a1 P x an Pn x 1 Legendre Polynomial These function are orthogonal over a range [ -1, 1 ]. This range can be scaled to fit the function. The orthogonal functions are defined as: 1 # if i j 1 Pi x Pj x dx 0 if i j Continuous Functions Other forms of orthogonal functions are sines and cosines, which are used in Fourier approximation. The advantages for the sines and cosines are that they can model large time scales. You will need to clip the ends of the series so that it will have zeros at the ends. Chapter 11 Numerical Differentiation and Integration Numerical differentiation A Taylor series or Lagrange interpolation of points can be used to find the derivatives. The Taylor series expansion is defined as: f xi f x0 x df x d 2 f 2 x 3 d 3 f dx x x 0 2! dx 2 x x0 3! dx 3 x x0 x xi x0 f xi f x0 xi x0 f x0 xi x0 2 f x0 xi x0 3 f x0 2! 3! Numerical differentiation Assume that the data points are equally spaced and the equations can be written as: f xi 1 f xi x f xi x 2 f x x 3 f xi 1 i 2! 3! f xi f xi 2 f xi-1 f xi x f xi x 2 f x x 3 f xi 3 i 2! 3! Differential Error Notice that the errors of the forward and backward 1st derivative of the equations have an error of the order of O(x) and the central differentiation has an error of order O(x2). The central difference has an better accuracy and lower error that the others. This can be improved by using more terms to model the first derivative. Higher Order Derivatives To find higher derivatives, use the Taylor series expansions of term and eliminate the terms from the sum of equations. To improve the error in the problem add additional terms. Lagrange Differentiation Another form of differentiation is to use the Langrange interpolation between three points. The values can be determine for unevenly spaced points. Given: Lx L1 x y1 L2 x y2 L3 x y3 x x2 x x3 y x x1 x x3 y x x1 x x2 y x1 x2 x1 x3 1 x2 x1 x2 x3 2 x3 x2 x3 x1 3 Lagrange Differentiation Differentiate the Lagrange interpolation 2 x x2 x3 f x Lx y1 x1 x2 x1 x3 2 x x1 x3 2 x x1 x2 y2 y3 x2 x1 x2 x3 x3 x2 x3 x1 Assume a constant spacing 2 x x2 x3 2 x x1 x3 2 x x1 x2 f x y1 y2 y3 2x 2 x 2 2x 2 Richardson Extrapolation This technique uses the concept of variable grid sizes to reduce the error. The technique uses a simple method for eliminating the error. Consider a second order central difference technique. Write the equation in the form: f xi 1 2 f xi f xi-1 f xi a1x a 2 x 2 4 x 2 Richardson Extrapolation The central difference can be defined as f xi 1 2 f xi f xi-1 f xi a1x a 2 x 2 4 x 2 Write the equation with different grid sizes A f xi Ax a1x 2 a 2 x 4 x x x 2 4 A f xi A a1 a2 2 2 2 Richardson Extrapolation The equation can be rewritten as: x 4 A Ax 2 x 4 a A 2 3 16 It can be rewritten in the form A Bx b1x b 2 x 4 6 Richardson Extrapolation The technique can be extrapolated to include the higher order error elimination by using a finer grid. x 16B Bx A 2 15 O x 6 Trapezoid Rule • Integrate to obtain the rule b b 1 a f ( x)dx L( x)dx h L( )d a 0 1 1 f (a )h (1 )d f (b)h d 0 0 1 2 1 2 h f (a )h ( ) f (b)h f (a ) f (b) 2 0 2 0 2 Simpson’s 1/3-Rule Integrate the Lagrange interpolation b 1 h 1 a f(x)dx h1 L( )dξ f(x0 ) 2 1 ξ(ξ 1 )dξ 1 h 1 f(x1 )h ( 1 ξ )dξ f(x2 ) ξ(ξ 1 )dξ 2 0 2 1 1 1 h ξ 3 ξ 2 ξ 3 f(x0 ) ( ) f(x1 )h(ξ ) 2 3 2 1 3 1 1 h ξ 3 ξ 2 f(x2 ) ( ) 2 3 2 1 f(x)dx f(x0 ) 4f(x1 ) f(x2 ) b h a 3 Simpson’s 3/8-Rule ( x x 1 )( x x 2 )( x x 3 ) L( x ) f ( x0 ) ( x 0 x 1 )( x 0 x 2 )( x 0 x 3 ) ( x x 0 )( x x 2 )( x x 3 ) f ( x1 ) ( x 1 x 0 )( x 1 x 2 )( x 1 x 3 ) ( x x 0 )( x x 1 )( x x 3 ) f ( x2 ) ( x 2 x 0 )( x 2 x 1 )( x 2 x 3 ) ( x x 0 )( x x 1 )( x x 2 ) f ( x3 ) ( x 3 x 0 )( x 3 x 1 )( x 3 x 2 ) b b b-a a f(x)dx a L(x)dx ; h 3 3h f ( x 0 ) 3 f ( x 1 ) 3 f ( x 2 ) f ( x 3 ) 8 Midpoint Rule Newton-Cotes Open Formula b a f ( x )dx ( b a ) f ( x m ) ab ( b a )3 (b a )f ( ) f ( ) 2 24 f(x) a xm b x Composite Trapezoid Rule b x1 x2 xn a f(x)dx f(x)dx f(x)dx x0 x1 xn 1 f(x)dx h f(x0 ) f(x1 ) h f(x1 ) f(x2 ) h f(xn1 ) f(xn ) 2 2 2 f(x0 ) 2 f(x1 ) 2f(xi ) 2 f ( x n 1 ) f ( x n ) h 2 f(x) ba h n x0 h x1 h x2 h x3 h x4 x Composite Simpson’s Rule Multiple applications of Simpson’s rule b x2 x4 xn a f(x)dx f(x)dx f(x)dx x0 x2 xn 2 f(x)dx f(x0 ) 4 f(x1 ) f(x2 ) f(x2 ) 4 f(x3 ) f(x4 ) h h 3 3 f(xn 2 ) 4f(xn 1 ) f(xn ) h 3 f(x0 ) 4 f(x1 ) 2f(x2 ) 4 f(x3 ) 2f(x4 ) h 3 4 f(x2i - 1 ) 2 f ( x 2 i ) 4f(x2i 1 ) 2 f ( x n 2 ) 4 f ( x n 1 ) f ( x n ) Richardson Extrapolation Use trapezoidal rule as an example – subintervals: n = 2j = 1, 2, 4, 8, 16, …. f(x)dx f(x 0 ) 2 f(x 1 ) 2 f ( x n 1 ) f ( x n ) c j h 2 j b h a 2 j 1 j n Form ula I 0 f ( a ) f ( b ) h 0 1 2 I 1 f ( a ) 2 f ( x 1 ) f ( b ) h 1 2 4 I 2 f ( a ) 2 f ( x 1 ) 2 f ( x 2 ) 2 f ( x 3 ) f ( b ) h 2 4 8 3 8 I3 h f ( a ) 2 f ( x 1 ) 2 f ( x 7 ) f ( b ) 16 j 2 j I j j f ( a ) 2 f ( x 1 ) 2 f ( x n 1 ) f ( b ) h 2 Richardson Extrapolation For trapezoidal rule b A f ( x )dx A( h ) c 1 h 2 a A A( h ) c 1 h 2 c 2 h 4 h h h A A( ) c 1 ( ) 2 c 2 ( ) 4 2 2 2 1 h c A 4 A( ) A( h ) 2 h 4 B( h ) b2 h 4 3 2 4 A B( h ) b2 h 4 1 h C( h ) 16 B( ) B( h ) 15 h h 4 A B( ) b2 ( ) 2 2 2 – kth level of extrapolation 4 C ( h/2) C ( h ) k D( h ) 4k 1 Romberg Integration Accelerated Trapezoid Rule 4 k I j 1 ,k I j ,k I j ,k ; k 1, 2, 3, 4 1 k Trapezoid Sim pson' s Boole' s k 0 k1 k2 k3 k4 O( h 2 ) O( h 4 ) O( h 6 ) O( h 8 ) O( h 10 ) h I 0 ,0 I 0 ,1 I 0,2 I 0,3 I 0 ,4 h/ 2 I 1,0 I 1,1 I 1, 2 I 1, 3 h/ 4 I 2 ,0 I 2 ,1 I 2,2 h/ 8 I 3 ,0 I 3 ,1 h / 16 I 4 ,0 4 I j 1,0 I j ,0 16 I j 1, 1 I j ,1 64 I j 1, 2 I j , 2 256 I j 1, 3 I j , 3 3 15 63 255 Gaussian Quadratures • Newton-Cotes Formulae – use evenly-spaced functional values • Gaussian Quadratures – select functional values at non-uniformly distributed points to achieve higher accuracy – change of variables so that the interval of integration is [-1,1] – Gauss-Legendre formulae Gaussian Quadrature on [-1, 1] 1 n2: 1 f(x)dx c 1 f(x 1 ) c 2 f(x 2 ) Exact integral for f = x0, x1, x2, x3 – Four equations for four unknowns f 1 1 1dx 2 c 1 c 2 c 1 1 1 c 1 1 2 f x xdx 0 c 1 x 1 c 2 x 2 1 1 12 x1 f x x dx c 1 x 1 c 2 x 2 2 2 2 2 3 1 3 1 f 3 1 x x 3 dx 0 c 1 x 1 c 2 x 2 3 3 x2 3 1 1 1 1 I f ( x )dx f ( ) f ( ) 1 3 3 Gaussian Quadrature on [-1, 1] Exact integral for f = x0, x1, x2, x3, x4, x5 1 5 3 8 5 3 I 1 f ( x )dx f ( 9 5 ) f (0 ) f ( 9 9 5 ) Summary • Open book and open notes. • The exam will be 5-8 problems. • Short answer type problems use a table to differentiate between techniques. • Problems are not going to be excessive. • Make a short summary of the material. • Only use your notes, when you have forgotten something, do not depend on them.

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