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Higham N.J. — Accuracy and Stability of Numerical Algorithms
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Название: Accuracy and Stability of Numerical Algorithms
Автор: Higham N.J.
Аннотация: What is the most accurate way to sum floating point numbers? What are the advantages of IEEE arithmetic? How accurate is Gaussian elimination and what were the key breakthroughs in the development of error analysis for the method? The answers to these and many related questions are included here.
This book gives a thorough, up-to-date treatment of the behavior of numerical algorithms in finite precision arithmetic. It combines algorithmic derivations, perturbation theory, and rounding error analysis. Software practicalities are emphasized throughout, with particular reference to LAPACK and MATLAB. The best available error bounds, some of them new, are presented in a unified format with a minimum of jargon. Because of its central role in revealing problem sensitivity and providing error bounds, perturbation theory is treated in detail.
Historical perspective and insight are given, with particular reference to the fundamental work of Wilkinson and Turing, and the many quotations provide further information in an accessible format.
The book is unique in that algorithmic developments and motivations are given succinctly and implementation details minimized, so that attention can be concentrated on accuracy and stability results. Here, in one place and in a unified notation, is error analysis for most of the standard algorithms in matrix computations. Not since Wilkinson's Rounding Errors in Algebraic Processes (1963) and The Algebraic Eigenvalue Problem (1965) has any volume treated this subject in such depth. A number of topics are treated that are not usually covered in numerical analysis textbooks, including floating point summation, block LU factorization, condition number estimation, the Sylvester equation, powers of matrices, finite precision behavior of stationary iterative methods, Vandermonde systems, and fast matrix multiplication.
Although not designed specifically as a textbook, this volume is a suitable reference for an advanced course, and could be used by instructors at all levels as a supplementary text from which to draw examples, historical perspective, statements of results, and exercises (many of which have never before appeared in textbooks). The book is designed to be a comprehensive reference and its bibliography contains more than 1100 references from the research literature.
Audience
Specialists in numerical analysis as well as computational scientists and engineers concerned about the accuracy of their results will benefit from this book. Much of the book can be understood with only a basic grounding in numerical analysis and linear algebra.
About the Author
Nicholas J. Higham is a Professor of Applied Mathematics at the University of Manchester, England. He is the author of more than 40 publications and is a member of the editorial boards of the SIAM Journal on Matrix Analysis and Applications and the IMA Journal of Numerical Analysis. His book Handbook of Writing for the Mathematical Sciences was published by SIAM in 1993.
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Рубрика: Математика /
Статус предметного указателя: Готов указатель с номерами страниц
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Издание: 2
Год издания: 2002
Количество страниц: 680
Добавлена в каталог: 19.06.2009
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Предметный указатель
Ng, Michael K. 457
Nickel, Karl 85 482 483
Nilson, E.N. 154
Nocedal, Jorge 226 468
Nonsymmetric positive definite matrix 208
Nonsymmetric positive definite matrix, LU factorization, stability of 208—209
Nordio, Marcelo 189
Norm 105—117
Norm , explicit formulae for 116
Norm consistent 108
Norm, 2-norm, evaluation without overflow 499—500
Norm, absolute 107
Norm, dual 107
Norm, Hoelder inequality 106
Norm, matrix 107—112
Norm, matrix norm equivalence constants 109
Norm, matrix p-norm 112—114
Norm, matrix p-norm of magic square matrix 115
Norm, monotone 107
Norm, Probenius 107
Norm, subordinate matrix 108 109
Norm, unitarily invariant 108
Norm, vector 106—107
Norm, vector norm equivalence constants 109
Normal distribution 3
Normal equations 382 405
Normal equations error analysis 386—388
Notation, explanation of 2—3 67—69
Notay, Yvan 324
NPSOL 189
Numerical analysis, definition 5—6
Numerical radius 343
Numerical stability definition 7 29
Numerical stability for linear equation solvers 129—130
O'Cinneide, Colm Art 133
O'Leary, Dianne Prost 302
Oberaigner, W. 88
Oettli — Prager backward error theorem 122
Oettli, W. 122 132
Ofman, Yu. 447
Olesky, D.D. 190
Oliver, J. 103 428
Olkin, Ingram 524 525
Olshevsky, V. 428 429
Olver, F.W.J. 49 69 77 102 187
Omladic, Matjaz 210
Opfer, Gerhard 429
Ordinary differential equations accuracy of mesh point formation 92
Ordinary differential equations, backward error in 29
Ordinary differential equations, Euler's method with compensated summation 86
Ortega, James M. 376
Osborne, M.R. 413
Ostrowski, A.M. 344 543
Outer product, error analysis 64—65
Overflow 16 38
Overflow, avoiding 499—501
Overton, Michael L. 35
P-norm power method 289—291 301—302
Paige, Christopher C. 190 209 371 376 377 380 386 397 403 404 407 413 414 565
Pair wise elimination 180 189
Pairwise (fan-in) summation 80
Palmer, John 522
Palmer, Kenneth J. 29
Pan, Ching-Tsuan 183
Pan, Victor Y. 278 282 429 436 437 446
Papadimitriou, Pythagoras 377
Papadopoulos, Philip M. 318
Parallel prefix operation 103 152
Paranoia code 495
Parhami, Behrooz 56
Park, Haesun 376
Parlett, Beresford N. xxviii xxx 24 30 55 76 79 184 215 226 353 375 376
Partial pivoting 158 162
Partial pivoting for skew-symmetric matrices 225
Partial pivoting for symmetric indefinite matrices 216—219
Partial pivoting growth factor 166—173
Partial pivoting growth factor, large growth in practical problems 167
Partial pivoting, early use of 188
Partial pivoting, threshold pivoting 193
Partitioned algorithm, definition 246
Partitioned LU factorization 246
Partitioned LU factorization, error analysis 249—250
Pascal matrix 518—521
Pascal matrix, Cholesky factor 519
Pascal matrix, eigenvalue reciprocity 519
Pascal matrix, inverse 520
Pascal matrix, total positivity 520
Pasternak, Mary E. 485
Patashnik, Oren 79 518
Paterson, Michael S. 102
Patrick, Merrell L. 227
Patriot missile software problem 503—504
Patterson, David A. 35 53
Paxson, Vern 57
Pei matrix 284 304
Pelz, Richard 502
Pena, J.M. 152 179 180 189 190
Pentium chip, division bug 55
Penzl, Thilo 317
Percival, Colin 457
Pereyra, Victor 423 423 425 429
Performance profile, for LAPACK norm estimator 294
Perturbation theory by calculus 132
Perturbation theory, linear systems 119—137
Perturbation theory, statistical 133 136
Perturbation theory, Sylvester equation 313—315
Peters, G. 103 188 275 281 282 404
Peterson, Brian 189
Philippe, Bernard 376
pi ( ), high-precision calculation as computer test 489
Pichat, M. 88
Pierce, Daniel J. 299
Pinkus, Allan 186 188
Plemmons, Robert J. 133 152 180 190 299 332 404 413
Polar decomposition 377 380
Poljak, Svatopluk 128
Polman, Ben 257
Polynomials 93—104; see also “Horner's method”
Polynomials, divided differences 99—101
Polynomials, fast evaluation schemes 103—104
Polynomials, matrix, evaluation of 102
Polynomials, Newton form 99—101
Ponceleon, Dulce B. 227
Poole, George 188
Poromaa, Peter 302 315 318
PORT library, machine constants 497
Portability of software 496—499
Positive definite matrix see “Nonsymmetric positive definite matrix” “Symmetric
Pothen, Alex 153
Powell, M.J.D. 210 362 375 395 404 472 474
Power method 22
Power method for matrix 1
Power method for matrix p-norm estimation 289—291 301—302
Power method, norm estimation 292—295
Power, Stephen 209
Powers of a matrix 339—352
Powers of a matrix in exact arithmetic 340—346
Powers of a matrix in finite precision arithmetic 346—350
Powers of a matrix, behaviour of stationary iteration 351
Powers of a matrix, departure from normality 344—345
Powers of a matrix, hump 342—343
Powers of a matrix, pseudospectrum 345—346 349—350
Powers of a matrix, role of spectral radius 340—342
Prager, W. 122 132
Precise 486
Precision effect of increasing 17—19
Precision versus accuracy 6 28
Preparata, F.P. 282
Press, William H. 476 487 505
Priest, Douglas M. 28 29 54 87—89 92 499 501 529
Program verification, applied to error analysis 485
Pryce, J.D. 69 241
Pseudo-inverse 382 402 405 408
Pseudospectral radius 345
Pseudospectrum 345—346 349—350
Pseudospectrum of companion matrix 523
Puglisi, Chiara 365
Pukelsheim, Friedrich 317
Puschmann, Heinrich 189
Pythagorean sum 500 507—509
QR factorization 355
QR factorization column pivoting 362 378
QR factorization iterative refinement for linear system 368—369
QR factorization perturbation theory 373—374
QR factorization rank-revealing 377
QR factorization row pivoting 362
QR factorization row sorting 362
QR factorization, generalized 397—398
QR factorization, Givens 365—366
QR factorization, Givens, cancellation of errors in 21—22
QR factorization, Givens, error analysis 366—368
QR factorization, Householder 355—357
QR factorization, Householder, error analysis 359—363
QR factorization, Householder, error analysis for application to LS problem 384—385
QR factorization, Householder, error analysis for partitioned (WY representation) 363—365
Quadratic equation, solving 10—11 29
Quadrature accuracy of grid formation 92
Quadrature error bound for evaluation of rule 78
Quasidefinite matrix see “Symmetric quasidefinite matrix”
Quinlan, Gerald D. 87
Quinn, Thomas R. 87
Quintana, Enrique S. 282
Quintana, Gregorio 282
Rabinowitz, Philip 90
Rail, Louis B. 402 483 485
Raimi, Ralph A. 48
Ramos, George U. 456
Random matrices 515—518
Random matrices with given singular values 517—518
Random matrices, 2-norm of 516
Random matrices, condition number of 516
Random matrices, correlation matrices 525
Random matrices, expected number of real eigenvalues 516—517
Random matrices, orthogonal 517 524
Random matrices, spectral radius of 516
Random matrices, tend to be well conditioned 516
Randsvd matrix 517—518 524 525
Range reduction 51
Rath, Wolfgang 376
Ratz, H.C. 323
Razaz, M. 102
RCOND condition estimator (LAPACK, LINPACK, MATLAB) 302 477—478
Recursively partitioned LU factorization 248
Reese, M.S. 298
Reichel, Lothar 103 350 428—430 525
Reichelt, Mark W. 524
Reid, John K. 180 181 186 190 193 227 362 375 395 400 404
Reinsch, C. xxix 281 57
Reiser, John F. 54
Relative error 3 4
Relative error counter, <k> 68
Relative error, componentwise 4
Relative precision 69
Relative residual 12
Ren, Huan 56
Renaut, R.E. 180
Research problems 92 104 193 212 229 242 304 319 352 406 430 449 469 487 525
Residual, relative 12
Rew, R.K. 287 297
Rheinboldt, Werner C. 467 467
Riccati equation, algebraic 316
Rice, John R. 29 376
Rigal — Gaches backward error theorem 120
Rigal, J.L. 120 132
Robertazzi, T.G. 89
Roberts, J.D. 317
Rohn, Jiff 116 128 135
Romani, Francesco 324
Rook pivoting 159—160 188
Rook pivoting for symmetric indefinite matrices 219—221
Rook pivoting, average number of comparisons 160 220
Rook pivoting, growth factor 170
Rose, Donald J. 257
Rosenthal, Peter 317
Ross, D.R. 88
Rounding 4 38
Rounding dealing with ties 38 54
Rounding error analysis model, standard 40
Rounding error analysis model, with underflow 56—57
Rounding error analysis model, without guard digit 44
Rounding error analysis notation 67—69
Rounding error analysis ordering of operations, effect of 70 141 142
Rounding error analysis purpose of 65 195
Rounding error analysis statistical approach 48—49
Rounding error analysis, automatic 471—487
Rounding error analysis, demystified 74—76
Rounding error analysis, graphs in 76
Rounding errors are not random 25—26 48
Rounding errors in subtraction 45
Rounding errors, accumulation of 14
Rounding errors, beneficial effects of 22—24
Rounding errors, cancellation of 19—22
Rounding errors, statistical assumptions on 48—49
Rounding modes in IEEE arithmetic 41
Rounding to even versus to odd 54
Rounding, monotonicity of 38
Rowan, Thomas Harvey 485 486
Rubin, Donald B. 381 402
Ruhe, Axel 376
Ruiz, Daniel 337
Rules of thumb condition for computed powers of matrix to converge to zero 350
Rules of thumb forward error related to backward error and condition number 9
Rules of thumb relative speed of floating point operations 56
Rules of thumb square root of constants in error bound 48
Rump, Siegfried M. 127—129 191 482 483
Runge — Kutta method 83 90
Running error analysis 66 486
Running error analysis for continued fraction 77
Running error analysis for Horner's method 95—96 103
Running error analysis for inner product 65—67
RussinofT, David M. 56
Rust, B.W. 132
Rutishauser, Heinz 512 518
Sadkane, Miloud 404
Sameh, Ahmed H. 150 151 180 485
Samelson, Klaus 53
Sample variance see “Variance”
Sande, G. 456
Sanz-Serna, J.M. 29
Saunders, Michael A. 226 227 229 257 413
Sautter, Werner 186
Scalapack 580
Scaling a linear system before Gaussian elimination 177—179 190
Scaling to minimize the condition number 123 125—127 178
Scarborough, James B. 28 54
Schatzman, James C. 456
Schaumburg, Kjeld 302
Schelin, Charles W. 50
Scherer, R. 76
Schneider, Hans 112 115 352
Schonfelder, J.L. 102
Schreiber, Robert S. 153 168 180 245 250—253 256 257 278 365 375 571
Schryer, N.L. 496
Schulz iteration 183 278
Schulz, Giinther 183 278
Schur complement 203 209 215 246 252
Schur complement, perturbation bounds for symmetric positive semidefinite matrix 203—208
Schwartz, S.C. 89
Schwetlick, Hubert 77 393 404 410 414
Searle, Shayle R. 317 487
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