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1 | (11) |
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1.1 Solving Real-world Problems |
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2 | (5) |
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1.2 A Short Recollection of Ideas |
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7 | (3) |
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1.3 Specification of the Subject and Basic Notation |
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10 | (2) |
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2 Krylov Subspace Methods |
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12 | (59) |
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12 | (7) |
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2.2 How Krylov Subspaces Come into Play |
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19 | (3) |
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2.3 Mathematical Characterisation of Some Krylov Subspace Methods |
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22 | (4) |
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2.4 The Arnoldi and Lanczos Algorithms |
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26 | (10) |
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2.4.1 Arnoldi and Hermitian Lanczos |
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26 | (5) |
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2.4.2 Non-Hermitian Lanczos |
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31 | (2) |
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2.4.3 Historical Note: The Gram-Schmidt Method |
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33 | (3) |
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2.5 Derivation of Some Krylov Subspace Methods |
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36 | (33) |
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2.5.1 Derivation of CG Using the Hermitian Lanczos Algorithm |
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36 | (6) |
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2.5.2 CG and the Galerkin Finite Element Method |
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42 | (5) |
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2.5.3 CG and the Minimisation of a Quadratic Functional |
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47 | (7) |
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2.5.4 Hermitian Indefinite Matrices and the SYMMLQ Method |
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54 | (3) |
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2.5.5 Minimising the Residual Norm: MINRES and GMRES |
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57 | (4) |
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2.5.6 Further Krylov Subspace Methods |
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61 | (3) |
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2.5.7 Historical Note: A. N. Krylov and the Early History of Krylov Subspace Methods |
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64 | (5) |
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2.6 Summary and Outlook: Linear Projections and Nonlinear Behaviour |
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69 | (2) |
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3 Matching Moments and Model Reduction View |
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71 | (97) |
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3.1 Stieltjes' Moment Problem |
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73 | (3) |
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3.2 Model Reduction via Orthogonal Polynomials |
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76 | (13) |
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3.2.1 Derivation of the Gauss-Christoffel Quadrature |
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77 | (7) |
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3.2.2 Moment Matching and Convergence Properties of the Gauss-Christoffel Quadrature |
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84 | (3) |
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3.2.3 Historical Note: Gauss' Fundamental Idea, an Early Application, and Later Developments |
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87 | (2) |
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3.3 Orthogonal Polynomials and Continued Fractions |
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89 | (19) |
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3.3.1 Three-term Recurrence and the Interlacing Property |
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89 | (7) |
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3.3.2 Continued Fractions |
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96 | (5) |
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3.3.3 The Gauss-Christoffel Quadrature for Analytic Functions |
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101 | (2) |
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3.3.4 Summary of the Previous Mathematical Development |
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103 | (1) |
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3.3.5 Historical Note: Chebyshev, Markov, Stieltjes, and the Moment Problem |
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104 | (3) |
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3.3.6 Historical Note: Orthogonal Polynomials and Three-term Recurrences |
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107 | (1) |
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108 | (28) |
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3.4.1 Algebraic Properties of Jacobi Matrices |
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109 | (12) |
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3.4.2 The Persistence Theorem, Stabilisation of Nodes and Weights in the Gauss-Christoffel Quadrature |
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121 | (9) |
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3.4.3 Historical Note: The Origin and Early Applications of Jacobi Matrices |
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130 | (6) |
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3.5 Model Reduction via Matrices: Hermitian Lanczos and CG |
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136 | (6) |
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3.6 Factorisation of the Matrix of Moments |
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142 | (3) |
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3.7 Vorobyev's Moment Problem |
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145 | (11) |
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3.7.1 Application to the Hermitian Lanczos Algorithm and the CG Method |
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146 | (2) |
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3.7.2 Application to the Non-Hermitian Lanczos Algorithm |
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148 | (3) |
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3.7.3 Application to the Arnoldi Algorithm |
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151 | (2) |
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3.7.4 Matching Moments and Generalisations of the Gauss-Christoffel Quadrature |
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153 | (3) |
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3.8 Matching Moments and Projection Processes |
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156 | (4) |
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3.9 Model Reduction of Large-scale Dynamical Systems |
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160 | (8) |
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3.9.1 Approximation of the Transfer Function |
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161 | (4) |
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3.9.2 Estimates in Quadratic Forms |
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165 | (3) |
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4 Short Recurrences for Generating Orthogonal Krylov Subspace Bases |
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168 | (59) |
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4.1 The Existence of Conjugate Gradient Like Descent Methods |
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169 | (3) |
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4.2 Cyclic Subspaces and the Jordan Canonical Form |
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172 | (17) |
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4.2.1 Invariant Subspaces and the Cyclic Decomposition |
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173 | (12) |
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4.2.2 The Jordan Canonical Form and the Length of the Krylov Sequences |
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185 | (3) |
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4.2.3 Historical Note: Classical Results of Linear Algebra |
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188 | (1) |
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4.3 Optimal Short Recurrences |
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189 | (4) |
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4.4 Sufficient Conditions |
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193 | (3) |
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196 | (8) |
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4.6 Matrix Formulation and Equivalent Characterisations |
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204 | (5) |
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4.7 Short Recurrences and the Number of Distinct Eigenvalues |
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209 | (2) |
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4.8 Other Types of Recurrences |
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211 | (11) |
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4.8.1 The Isometric Arnoldi Algorithm |
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211 | (4) |
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4.8.2 (s + 2, t)-term Recurrences |
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215 | (4) |
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4.8.3 Generalised Krylov Subspaces |
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219 | (3) |
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4.9 Remarks on Functional Analytic Representations |
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222 | (5) |
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5 Cost of Computations Using Krylov Subspace Methods |
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227 | (122) |
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5.1 Seeing the Context is Essential |
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228 | (10) |
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5.2 Direct and Iterative Algebraic Computations |
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238 | (7) |
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5.2.1 Historical Note: Are the Lanczos Method and the CG Method Direct or Iterative Methods? |
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241 | (4) |
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5.3 Computational Cost of Individual Iterations |
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245 | (3) |
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5.4 Particular Computations and Complexity |
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248 | (2) |
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5.5 Closer Look at the Concept of Convergence |
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250 | (8) |
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5.5.1 Linear Stationary Iterative Methods |
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250 | (2) |
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5.5.2 Richardson Iteration and Chebyshev Semi-iteration |
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252 | (2) |
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5.5.3 Historical Note: Semi-iterative and Krylov Subspace Methods |
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254 | (3) |
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5.5.4 Krylov Subspace Methods: Nonlinear Methods for Linear Problems |
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257 | (1) |
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5.6 CG in Exact Arithmetic |
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258 | (27) |
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5.6.1 Expressions for the CG Errors and Their Norms |
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260 | (5) |
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5.6.2 Eigenvalue-based Convergence Results for CG |
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265 | (6) |
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5.6.3 Illustrations of the Convergence Behaviour of CG |
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271 | (4) |
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5.6.4 Outlying Eigenvalues and Superlinear Convergence |
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275 | (5) |
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5.6.5 Clustered Eigenvalues and the Sensitivity of the Gauss-Christoffel Quadrature |
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280 | (5) |
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5.7 GMRES in Exact Arithmetic |
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285 | (23) |
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5.7.1 Ritz Values and Harmonic Ritz Values |
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286 | (3) |
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5.7.2 Convergence Descriptions Based on Spectral Information |
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289 | (5) |
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5.7.3 More General Approaches |
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294 | (3) |
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5.7.4 Any Nonincreasing Convergence Curve is Possible with Any Eigenvalues |
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297 | (6) |
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5.7.5 Convection-diffusion Example |
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303 | (3) |
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5.7.6 Asymptotic Estimates for the GMRES Convergence |
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306 | (2) |
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5.8 Rounding Errors and Backward Stability |
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308 | (12) |
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5.8.1 Rounding Errors in Direct Computations |
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311 | (4) |
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5.8.2 Historical Note: Wilkinson and the Backward Error Concept |
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315 | (1) |
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5.8.3 The Backward Error Concept in Iterative Computations |
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316 | (4) |
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5.9 Rounding Errors in the CG Method |
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320 | (18) |
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5.9.1 Delay of Convergence |
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320 | (6) |
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5.9.2 Delay of Convergence can Invalidate Composite Convergence Bounds |
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326 | (2) |
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5.9.3 Maximal Attainable Accuracy |
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328 | (3) |
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5.9.4 Back to the Poisson Model Problem |
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331 | (7) |
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5.10 Rounding Errors in the GMRES Method |
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338 | (7) |
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5.10.1 The Choice of the Basis Affects Numerical Stability |
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338 | (2) |
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5.10.2 Does the Orthogonalisation Algorithm Matter? |
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340 | (3) |
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5.10.3 MGS GMRES is Backward Stable |
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343 | (2) |
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5.11 Omitted Issues and an Outlook |
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345 | (4) |
References |
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349 | (36) |
Index of Historical Personalities |
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385 | (3) |
Index of Technical Terms |
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388 | |