scheme Aitken-Neville, 648 Horner, 632 Schur Komplement, 95 Schur’s lemma, 399 scientific notation, 107 secant condition, 323 secant method, 288, 323 segmentation of an image, 427 semi-implicit Euler method, 933 seminorm, 669 sensitivity of a problem, 136 shape preservation, 696 preserving spline interpolation, 735 Sherman-Morrison-Woodbury formula, 218 shifted inverse iteration, 440 similarity of matrices, 398 complete cubic, 728 cubic, 722 cubic, locality, 731 natural cubic, 728 periodic cubic, 729 shape preserving interpolation, 735 stability function of explicit Runge-Kutta methods, 909 of Runge-Kutta methods, 930 Stable algorithm, 120 stable numerically, 120 state space of an ODE, 830 stationary distribution, 413 steepest descent, 337 stiff IVP, 925 stochastic matrix, 414 stochastic simulation of page rank, 412 Strassen’s algorithm, 42 structurally symmetric matrix, 172 similarity transformations, 398 similary transformation unitary, 402 Simpson rule, 769 sine basis, 605 matrix, 605 transform, 606 Sine transform, 605 single precicion, 109 single step method, 848 singular value decomposition, 481, 484 sparse matrix, 139 initialization, 146 LU-factorization, 152 multiplication, 149 sparse matrix storage formats, 141 spectral radius, 397 spectrum, 397 of a matrix, 343 spline cardinal, 731 sub-matrix, 26 sub-multiplicative, 116 subspace correction, 350 subspace iteration for direct power method, 455 subspaces nested, 350 SVD, 481, 484 symmetry structural, 172 system matrix, 66, 119 system of equations linear, 66 tagent field, 830 Taylor expansion, 271 Taylor’s formula, 271 tensor product, 31 Teopltiz matrices, 617 termination criterion, 251 Newton iteration, 315 reliable, 252 Ôº ½¿º Ôº ½¿º residual based, 253 time-invariant filter, 541 timestep constraint, 910 Toeplitz solvers fast algorithms, 624 tolerace absolute, 276 tolerance, 253 absolute, 878 for adaptive timestepping for ODEs, 876 realtive, 878 total least squares, 524 trajectory, 825 transform cosine, 614 fast Fourier, 594 sine, 606 trapezoidal rule, 769, 862 for ODEs, 862 triangle inequality, 115 tridiagonal matrix, 164 trigonometric basis, 562 List of Symbols (x k )∗ n (y k ) ˆ= discrete periodic convolution, 548 DΦ ˆ= Jacobian of Φ : D ↦→ R n at x ∈ D, 265 D y f ˆ= Derivative of f w.r.t.. y (Jacobian), 836 J(t 0 ,y 0 ) ˆ= maximal domain of definition of a solution of an IVP, 837 O ˆ= zero matrix, 27 O(n), 41 E ˆ= expected value of a random variable, 619 R k (m, n), 496 eps ˆ= machine precision, 112 Eig A (λ) ˆ= eigenspace of A for eigenvalue λ, 397 Im(A) ˆ= range/column space of matrix A, 488 Ker(A) ˆ= nullspace of matrix A, 488 trigonometric transformations, 604 trust region method, 538 underflow, 110 unit vector, 24 unitary matrix, 194 unitary similary transformation, 402 upper Hessenberg matrix, 473 upper triangular matrix, 74, 85, 86 Vandermonde matrix, 638 Weddle rule, 770 weight quadrature, 764 well conditioned, 136 Zerlegung LU, 93 QR, 227 zero padding, 553 K l (A,z) ˆ= Krylov subspace, 353 ‖A‖ 2 F , 496 ‖x‖ A ˆ= energy norm induced by s.p.d. matrix A, 333 ‖f‖ L ∞ (I) , 641 ‖f‖ L 1 (I) , 642 ‖f‖ 2 L 2 (I) , 642 P k , 631 Ψ h y ˆ= discretei evolution for autonomous ODE, 848 S d,M , 721 A + , 505 I ˆ= identity matrix, 27 h ∗ x ˆ= discrete convolution of two vectors, 545 Ôº ½¿º Ôº ½¿º
x∗ n y ˆ= discrete periodic convolution of vectors, 548 M ˆ= set of machine numbers, 106 δ ij ˆ= Kronecker symbol, 24 κ(A) ˆ= spectral condition number, 348 λ max ˆ= largest eigenvalue (in modulus), 348 λ min ˆ= smalles eigenvalue (in modulus), 348 1 = (1,...,1) T , 909, 930 Ncut(X) ˆ= normalized cut of subset of weighted graph, 430 cond(A), 132 cut(X) ˆ= cut of subset of weighted graph, 430 env(A), 166 nnz, 139 rank(A) ˆ= rank of matrix A, 70 rd, 111 weight(X) ˆ= connectivity of subset of weighted graph, 430 m(A), 164 ρ(A) ˆ= spectral radius of A ∈ K n,n , 397 ρ A (u) ˆ= Rayleigh quotient, 423 f ˆ= right hand side of an ODE, 830 σ(M) hat= spectrum of matrix M, 343 ˜⋆, 111 m(A), 164 m(A), 164 y[t i , ...,t i+k ] ˆ= divided difference, 658 ‖ x ‖ 1 , 115 ‖ x ‖ 2 , 115 ‖ x ‖ ∞ , 115 ˙ ˆ= Derivative w.r.t. time t, 823 TOL tolerance, 876 Ôº ½¿º Evolution operator, 839 Explicit Runge-Kutta method, 864 Fill-In, 153 Frobenius norm, 496 function concave, 691 convex, 691 Hessian matrix, 306 Inverse of a matrix, 70 Krylov space, 353 L-stable Runge-Kutta method, 931 Lebesgue constant, 643 Legendre polynomials, 799 Linear convergence, 243 Lipschitz continuos function, 835, 836 machine numbers, 108 matrix generalized condition number, 506 s.p.d, 179 Single step method, 848 symmetric positive definite, 179 Matrix envelope, 166 matrix norm, 116 monotonic data, 689 norm, 115 Frobenius norm, 496 Normalized cut, 430 numerical algorithm, 119 Order of convergence, 247 orthogonal matrix, 194 Permutation matrix, 102 polynomial Bernstein, 754 Chebychev, 671 generalized Lagrange, 640 pseudoinverse, 505 Rank of a matrix, 70 Rayleigh quotient, 423 residual, 126 Runge-Kutta method, 928 Ôº½ ½¿º singular value decomposition (SVD), 484 sparse matrices, 139 List of Definitions sparse matrix, 139 splines, 721 stable algorithm, 120 Arrow matrix, 156 Bézier curves, 757 Bernstein polynomials, 754 Chebychev polynomial, 671 circulant matrix, 551 concave data, 690 function, 691 Condition (number) of a matrix, 132 Consistency of fixed point iterations, 259 Consistency of iterative methods, 240 Contractive mapping, 263 Convergence, 240 local, 241 convex data data, 690 function, 691 concave, 690 convex, 690 Diagonally dominant matrix, 182 Discrete convolution, 545 discrete Fourier transform, 565 discrete periodic convolution, 548 eigenvalues and eigenvectors, 397 energy norm, 333 Ôº¼ ½¿º Structurally symmetric matrix, 172 Toeplitz matrix, 620 Types of matrices, 86 unitary matrix, 194 Ôº¾ ½¿º global, 241 equivalence of norms, 243
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2 Direct Methods for Linear Systems
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III Integration of Ordinary Differe
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Extra questions for course evaluati
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1.1.2 Matrices Matrices = two-dimen
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Remark 1.2.1 (Row-wise & column-wis
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1.3 Complexity/computational effort
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Syntax of BLAS calls: The functions
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4 { 5 a ssert ( this−>n==B. n &&
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34 long r t 0 ; 35 bool bStarted ;
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Obviously, left multiplication with
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❶: elimination step, ❷: backsub
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A direct way to LU-decomposition:
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Solution of LŨx = b: x ( ) 2ǫ = 1
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numerically equivalent ˆ= same res
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Code 2.4.8: Finding outeps in MATLA
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Terminology: Def. 2.5.5 introduces
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Example 2.5.5 (Instability of multi
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Note: sensitivity gauge depends on
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6 for i =1:20 7 n = 2^ i ; m = n /
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0 20 40 60 80 100 120 140 160 180 2
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Use sparse matrix format: 10 1 10 2
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Envelope-aware LU-factorization: 0
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0 20 40 60 80 100 0 20 0 20 0 20 De
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Evident: symmetry of à − bbT a 1
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9 ylabel ( ’ { \ b f c o n d i t
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Mapping a ∈ K n to a multiple of
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Then store G ij (a,b) as triple (i,
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Recall: e i ˆ= i-th unit vector Ch
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Computation of Choleskyfactorizatio
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1 ∃ (partial) cyclic row permutat
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Definition 3.1.3 (Local and global
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Example 3.1.6 (quadratic convergenc
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k |x (k) − π| L 1−L |x (k) −
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(x (k) ) k∈N0 Cauchy sequence ➤
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Termination criterion for contracti
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Given x (k) ∈ I, next iterate :=
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secant method ( MATLAB implementati
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Assuming p = 1: p > 1: ∥ C ∥e (
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This is a simple computation: DG(x)
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k x (k) ǫ k := ‖x ∗ − x (k)
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Code 3.4.14: Damped Newton method (
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MATLAB-CODE: Broyden method (3.4.11
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Algorithm 4.1.3 (Steepest descent).
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Example 4.1.8 (Convergence of gradi
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4.2.1 Krylov spaces Definition 4.2.
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Remark 4.2.3 (A posteriori terminat
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10 figure ; view ([ −45 ,28]) ; m
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Idea: Solve Ax = b approximately in
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eplaced with κ(A) ! 4.4.2 Iteratio
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For circuit of Fig. 55 at angular f
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(Linear) generalized eigenvalue pro
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10 0 10 1 matrix size n d = eig(A)
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0 50 100 150 200 250 300 350 400 45
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1 2 3 k ρ (k) EV ρ (k) EW ρ (k)
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✬ ✩ ✬ ✩ Lemma 5.3.4 (Ncut a
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In other words, roundoff errors may
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Theory: linear convergence of (5.3.
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error in eigenvalue 10 0 10 −2 10
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✬ Residuals r 0 ,...,r m−1 gene
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Algebraic view of the Arnoldi proce
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5.5 Singular Value Decomposition Re
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Illustration: columns = ONB of Im(A
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✬ Theorem 5.5.7 (best low rank ap
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Reassuring: Remark 6.0.4 (Pseudoinv
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Consider the linear least squares p
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Goal: Euclidean distance of y ∈ R
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6.5 Non-linear Least Squares If (6.
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0 2 4 6 8 10 12 14 16 value of ∥
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Definition 7.1.1 (Discrete convolut
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Expand a 0 ,...,a n−1 and b 0 , .
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(7.2.2) is a simple consequence of
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Dominant coefficients of a signal a
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11 c = f f t ( y ) ; 12 13 figure (
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Two-dimensional trigonometric basis
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8 end 9 t1 = min ( t1 , toc ) ; 10
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Step II: for k =: rq + s, 0 ≤ r <
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MATLAB-CODE Sine transform function
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△ Example 7.5.2 (Linear regressio
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[23, Ch. IX] presents the topic fro
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Code 8.1.3: Horner scheme, polynomi
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1.2 equality in (8.2.10) for y := (
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ecursive definition: p i (t) ≡ y
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a 1 = y 1 − a 0 t 1 − t 0 = y 1
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Observations: Strong oscillations o
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−1 −0.8 −0.6 −0.4 −0.2 0
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8.5.3 Chebychev interpolation: comp
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9.1 Shape preserving interpolation
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9.2.2 Piecewise polynomial interpol
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Interpolation of the function: f(x)
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2 % Plot convergence of approximati
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9.4 Splines Definition 9.4.1 (Splin
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➤ Linear system of equations with
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y i+1 t i−1 t i t i+1 y i−1 y i
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35 h= d i f f ( t ) ; 36 d e l t a
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1 0.9 Function f 1 0.9 Function f
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