Sprzedam książkę w stanie  dobrym +  :

Computational mathematics / B. P. Demidovich, I. A. Maron ; transl. from the Russian by George Yankovsky.

Wydanie: 3rd print

Adres wydawniczy  Moscow : Mir, 1981.

Opis fizyczny  688 s. : il. ; 23 cm.

Temat  Analiza numeryczna.

Unieważnione pieczęcie. Książka cała, czysta. W środku nie nosi śladów użytkowania.

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The basic aim of this book is to give as far as possible a

systematic and modern presentation of the most important methods and  techniques of computational mathematics on the basis of the general  course of higher mathematics taught in higher technical schools. The  book has been arranged so that the basic portion constitutes a  manual for the first cycle of studies in approximate computations  for higher technical colleges. The text contains supplementary  material which goes beyond the scope of the ordinary college course,  but the reader can select those sections which interest him and omit  any extra material without loss of continuity. The chapters and  sections which may be dropped out in a first reading are marked with an asterisk.

For a full comprehension of the contents of this book, the reader

should have a background of linear algebra and the theory of linear

vector spaces. With the aim of making the text as self-contained as

possible, the authors have included all the necessary starting

material in these subjects. The appropriate chapter are completely

independent of the basic text and can be omitted by readers who have  already studied these sections.

A few words about the contents of the book. In the main it is

devoted to the following problems: operations involving approximate  numbers, computation of functions by means of series and iterative  processes, approximate and numerical solution of algebraic and  transcendental equations, computational methods of linear algebra,  interpolation of functions, numerical differentiation and  integration of functions, and the Monte Carlo method.

This book was translated from the Russian by George Yankovsky. The book was  published by first Mir Publishers in 1973,

Table of Contents

PREFACE

INTRODUCTION.

GENERAL RULES OF COMPUTATIONAL WORK

CHAPTER 1

APPROXIMATE  NUMBERS 19

1.1 Absolute and relative errors 19

1.2 Basic sources of errors  22

1.3 Scientific notation. Significant digits, The number of correct

digits 23

1.4 Rounding of numbers  26

1.5 relationship between the relative error of an approximate number

and the number of correct digits  27

1.6 Tables for determining the limiting relative error from the number

of correct digits and vice versa 30

1.7 The error of a sum 33

1.8 The error of a difference 35

1.9 The error of a product 37

1.10 The number of correct digits in a product 39

1.11 The error of a quotient 40

1.12 The number of correct digits in a quotient 41

1.13 The relative error of a power 41

1.14 The relative error of a root 41

1.15 Computations in which errors are not taken into exact account 42

1.16 General formula for errors 42

1.17 The inverse problem of the theory of errors 44

1.18 Accuracy in the determination of arguments from a tabulated

function 48

1.19 The method of bounds 50

1.20 The notion of a probability error estimate 52

References for Chapter 1 54

CHAPTER 2

SOME FACTS FROM THE THEORY OF CONTINUOUS FRACTIONS 55

2.1 The definition of a continued fraction 55

2.2 Converting a continued fraction to a simple fraction and vice

versa 56

2.3 Convergents 58

2.4 Nonterminating continued fractions 66

2.5 Expanding functions into continued fractions 72

References for Chapter 2 76

CHAPTER 3

COMPUTING THE VALUES OF FUNCTIONS 77

3.1 Computing the values of a polynomial. Horner’s scheme  77

3.2 The generalized Horner scheme 80

3.3 Computing the values of rational fractions 82

3.4 Approximating the sums of numerical series 83

3.5 Computing the values of an analytic function 89

3.6 Computing the values of exponential functions 91

3.7 Computing the values of a logarithmic function 95

3.8 Computing the values of trigonometric functions 98

3.9 Computing the values of hyperbolic functions 101

3.10 Using the method of iteration for approximating the values of

function 103

3.11 Computing reciprocals 104

3.12 Computing square roots 107

3.13 Computing the reciprocal of a square root 111

3.14 Computing cube roots 112

References for Chapter 3 114

CHAPTER 4

APPROXIMATE SOLUTIONS OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS 115

4.1 Isolation of roots 115

4.2 Graphical solution of equations 119

4.3 The halving method 121

4.4 The method of proportional parts (method of chords) 122

4.5 Newton’s method {method of tangents) 127

4.6 Modified Newton method 135

4.7 Combination method 136

4.8 The method of iteration 138

4.9 The method of iteration for a system of two equations 152

4.10 Newton’s method for a’system of two equations 156

4.11 Newton’s method for the case of complex roots 157

References for Chapter 5 161

CHAPTER 5

SPECIAL TECHNIQUES FOR APPROXIMATE SOLUTION OF EQUATIONS 162

5.1 General properties of algebraic equations 162

5.2 The bounds of real roots of algebraic equations 167

5.3 The method of alternating sums 169

5.4 Newton’s method 171

5.5 The number of real roots of a polynomial 173

5.6 The theorem of Budan-Fourier 175

5.7 The underlying principle of the method of Lobachevsky-Graeife 179

5.8 The root-squaring process 182

5.9 The Lobachevsky-Graeffe method for the case of real and distinct

roots 184

5.10 The Lobachevsky-Graeife method for the case of complex roots 187

5.11 The case of a pair of complex roots 190

5.12 The case of two pairs of complex roots 194

5.13 Bernoulli’s method 198

References for Chapter 5 202

CHAPTER 6

ACCELERATING THE CONVERGENCE OF SERIES 203

6.1 Accelerating the convergence of numerical series 203

6.2 Accelerating the convergence of power series by the Euler-Abel

method 209

6.3 Estimates of Fourier coefficient 213

6.4 Accelerating the convergence of Fourier trigonometric series by

the method of A, N. Krylov 217

6.5 Trigonometric approximation 225

References for Chapter 6 228

CHAPTER 7

MATRIX ALGEBRA 229

7.1 Basic definitions 229

7.2 Operations involving matrices 230

7.3 The transpose of a matrix 234

7.4 The inverse matrix 236

7.5 Powers of a matrix 240

7.6 Rational functions of a matrix 241

7.7 The absolute value and norm of a matrix 242

7.8 The rank of a matrix 248

7.9 The limit of a matrix 249

7.10 Series of matrices 251

7.11 Partitioned matrices 256

7.12 Matrix inversion by partitioning 260

7.13 Triangular matrices 265

7.14 Elementary transformations of matrices 268

7.15 Computation of determinants 269

References for Chapter 7 272

CHAPTER 8

SOLVING  SYSTEMS OF LINEAR EQUATIONS 273

8.1 A general description of methods of solving systems of linear

equations 273

8.2 Solution by inversion of matrices. Cramer’s rule 273

8.3 The Gaussian method 277

8.4 Improving roots  284

8.5 The method of principal elements 287

8.6 Use of the Gaussian method in computing determinants 288

8.7 Inversion of matrices by the Gaussian method 290

8.8 Square-root method 293

8.9 The scheme of Khaletsky 296

8.10 The method of iteration 300

8.11 Reducing a linear system to a form convenient for iteration 307

8.12 The Seidel method 309

8.13 The case of a normal system 311

8.14 The method of relaxation 313

8.15 Correcting elements of an approximate inverse matrix 316

References for Chapter 8 321

CHAPTER 9

THE CONVERGENCE OF ITERATION PROCESSES FOR SYSTEMS OF LINEAR EQUATIONS 322

9.1 Sufficient conditions for the convergence of the iteration process 322

9.2 An estimate of the error of approximations in the iteration

process 324

9.3 First sufficient condition for convergence of the Seidel process 327

9.4 Estimating the error of approximations in the Seidel process by the m-norm 330

9.5 Second sufficient condition for convergence of the Seidel process 330

9.6 Estimating the error of approximations in the Seidei process by

the l-norm 332

9.7 Third sufficient condition for convergence of the Seidel process 333

References for Chapter 9 335

CHAPTER 10

ESSENTIALS OF THEORY OF LINEAR VECTOR SPACES 336

10.1 The concept of a linear vector space 336

10.2 The linear dependence of vectors 337

10.3 The scalar product of vectors 343

10.4 Orthogonal systems of vectors 345

10.5 Transformations of the coordinates of a vector the basis 348

10.6 Orthogonal matrices 350

10.7 Orthogonalization of matrices 351

10.8 Applying orthogonalixation methods to the solutions of linear

equations 358

10.9 The solution space of a homogeneous system 364

10.10 Linear transformations of variables 367

10.11 Inverse transformation 373

10.12 Eigenvectors and eigenvalues of a matrix 375

10.13 Similar matrices 380

10.14 Bilinear form of a matrix 384

10.15 Properties of symmetric matrices 384

10.16 Properties of matrices with real elements 389

References for Chapter 10 393

CHAPTER 11

ADDITIONAL FACTS ABOUT THE CONVERGENCE OF ITERATION PROCESSES FOR

SYSTEMS OF LINEAR EQUATIQHS 394

11.1 The convergence of matrix power series 394

11.2 The Cayley-Hamilton theorem 397

11.3 Necessary and sufficient conditions for the convergence of the

process of iteration for a system of linear equations 398

11.4 Necessary and sufficient conditions for the convergence of the

Seidel process for a system of linear equations 400

11.5 Convergence of the Seidel process for a normal system 403

11.6 Methods for effectively checking the conditions of convergence 405

References for Chapter 11 409

CHAPTER 12

FINDING THE EIGENVALUES AND EIGENVECTORS OF A MATRIX 410

12.1 Introductory remarks 410

12.2 Expansion of secular determinants 410

12.3 The method of Danilevsky 412

12.4 Exceptional cases in the Danilevsky method 418

12.5 Computation of eigenvectors by the Danilevsky method 420

12.6 The method of Krylov 421

12.7 Computation of eigenvectors by the Krylov method 424

12.8 Leverrier’s method 426

12.9 On the method of undetermined coefficients 428

12.10 A comparison of different methods of expanding a secular

determinant 429

12.11 Finding the numerically largest eigenvalue of a matrix and the

corresponding eigenvector 430

12.12 The method of scalar products for finding the first eigenvalue

of a real matrix 436

12.13 Finding the second eigenvalue of a matrix and the second

eigenvector 439

12.14 The method of exhaustion 443

12.15 Finding the eigenvalues and eigenvectors of a positive definite

symmetric matrix 445

12.16 Using the coefficients of the characteristic polynomial of a

matrix for matrix inversion 450

12.17 The method of Lyusternik for accelerating the convergence of the

iteration process in the solution of a system of linear equation 453

References for Chapter 12  458

CHAPTER 13

APPROXIMATE SOLUTION OF SYSTEMS OF NOHLINEAR EQUATIONS  459

13.1 Newton’s method 459

13.2 General remarks on the convergence of the Newton process 465

13.3 The existence of roots of a system and the convergence of the

Newton process 469

13.4 The rapidity of convergence of the Newton process 474

13.5 Uniqueness of solution 475

13.6 Stability of convergence of the Newton process under variations

of the initial approximation 478

13.7 The modified Newton method 481

13.8 The method of iteration 484

13.9 The notion of a contraction mapping 487

13.10 First sufficient condition for the convergence of the process of

iteration 491

13.11 Second sufficient condition for the convergence of the process

of iteration 493

13.12 The method of steepest descent (gradient method) 496

13.13 The method of steepest descent for the case of a system of

linear equations 501

13.14 The method of power series 504

References for Chapter 13 506

CHAPTER 14

THE INTERPOLATION OF FUNCTIONS 507

14.1 Finite differences of various orders 507

14.2 Difference table 510

14.3 Generalized power 517

14.4 Statement of the problem of interpolation 518

14.5 Newton’s first interpolation formula 519

14.6 Newton’s second interpolation formula 526

14.7 Table of central differences 530

14.8 Gaussian interpolation formulas 531

14.9 Stirling’s interpolation formula 533

14.10 Bessel’s interpolation formula 534

14.11 General description of interpolation formulas with constant

interval 536

14.12 Lagrange’s interpolation formula 539

14.13 Computing Lagrangian coefficients 543

14.14 Error estimate of Lagrange’s interpolation formula 547

14.15 Error estimates of Newton’s interpolation formulas 550

14.16 Error estimates of the central interpolation formulas 552

14.17 On the best choice of interpolation points 553

14.18 Divided differences 554

14.19 Newton’s interpolation formula for unequally spaced values of

the argument 556

14.20 Inverse interpolation for the case of equally spaced points 559

14.21 Inverse interpolation for the case of unequally spaced points 562

14.22 Finding the roots of an equation by inverse interpolation 564

14.23 The interpolation method for expanding a secular determinant 565

14.24 Interpolation of functions of two variables 567

14.25 Double differences of higher order 570

14.26 Newton’s interpolation formula for a function of two variables 571

References for Chapter 14 573

CHAPTER 15

APPROXIMATE DIFFERENTIATION 574

15.1 Statement of the problem 574

15.2 Formulas of approximate differentiation based on Newton’s first

interpolation formula 575

15.3 Formulas of approximate differentiation based on Stirling’s

formula 580

15.4 Formulas of numerical differentiation for equally spaced points 583

15.5 Graphical differentiation 586

15.6 On the approximate calculation of partial derivatives 588

References for Chapter 15  589

CHAPTER 16

APPROXIMATE INTEGRATION OF FUNCTIONS 590

16.1 General remarks 590

16.2 Newton-Cotes quadrature formulas 593

16.3 The trapezoidal formula and its remainder term 595

16.4 Simpson’s formula and its remainder term 596

16.5 Newton-Cotes formulas of higher orders 599

16.6 General trapezoidal formula (trapezoidal rule) 601

16.7 Simpson’s general formula (parabolic rule) 603

16.8 On Chebyshev’s quadrature formula 607

16.9 Gaussian quadrature formula 611

16.10 Some remarks on the accuracy of quadrature formulas 618

16.11 Richardson extrapolation 622

16.12 Bernoulli numbers 625

16.13 Euler-Maclaurin formula 628

16.14 Approximation of improper integrals 633

16.15 The method of Kantorovich for isolating singularities 635

16.16 Graphical integration 639

16.17 On cubature formulas 641

16.18 A cubature formula of Simpson type 644

References for Chapter 16 648

CHAPTER 17

THE MONTE CARLO METHOD 649

17.1 The idea of the Monte Carlo method 649

17.2 Random numbers 650

17.3 Ways of generating random numbers 653

17.4 Monte Carlo evaluation of multiple integrals 656

17.5 Solving systems of linear algebraic equations method by the Monte

Carlo method 666

References for Chapter 17 674

COMPLETE LIST OF REFERENCES 675

INDEX 679