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The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines bringing the total to well over 300, plus upgraded versions of the original routines, the new edition remains the most practical, comprehensive handbook of scientific computing available today.
Note: This edition only includes the diskette.
A considerable (50%) expansion of what has come to be the handbook of first resort in scientific computation. A few new chapters were added, and, mainly, many more topics/techniques are now concisely discussed. A great addition to any reference collection, even if you already have the first edition. The text comes in both C and Fortran editions. Cambridge also makes available the following companion products, which our customers have found extremely valuable in their own right: Example books---- tutorial guides to using the routines within your own programs Program disks---- all 300+ routines from the text itself, plus sample programs from the example books. For the basic book, example books, and disks, be sure to specify whether Fortran or C editions are preferred; for the disks, be sure to specify IBM (3.5" or 5.25") or Macintosh formats.
whose solution or solutions are desired. When there is only one independent variable, the problem is one-dimensional, namely to find the root or roots of a function.
With more than one independent variable, more than one equation can be satisfied simultaneously. You likely once learned the implicit function theorem which (in this context) gives us the hope of satisfying N equations in N unknowns simultaneously. Note that we have only hope, not certainty. A nonlinear set of equations may have no (real) solutions at all. Contrariwise, it may have more than one solution. The implicit function theorem tells us that "generically" the solutions will be distinct, pointlike, and separated from each other. If, however, life is so unkind as to present you with a nongeneric, i.e., degenerate, case, then you can get a continuous family of solutions. In vector notation, we want to find one or more N-dimensional solution vectors x such that
where f is the N-dimensional vector-valued function whose components are the individual equations to be satisfied simultaneously.
Don't be fooled by the apparent notational similarity of equations (9.0.2) and (9.0.1). Simultaneous solution of equations in N dimensions is much more difficult than finding roots in the one-dimensional case. The principal difference between one and many dimensions is that, in one dimension, it is possible to bracket or "trap" a root between bracketing values, and then hunt it down like a rabbit. In multidimensions, you can never be sure that the root is there at all until you have found it.
Except in linear problems, root finding invariably proceeds by iteration, and this is equally true in one or in many dimensions. Starting from some approximate trial solution, a useful algorithm will improve the solution until some predetermined convergence criterion is satisfied. For smoothly varying functions, good algorithms will always converge, provided that the initial guess is good enough. Indeed one can even determine in advance the rate of convergence of most algorithms.
It cannot be overemphasized, however, how crucially success depends on having a good first guess for the solution, especially for multidimensional problems. This crucial beginning usually depends on analysis rather than numerics. Carefully crafted initial estimates reward you not only with reduced computational effort, but also with understanding and increased self-esteem. Hamming's motto, "the purpose of computing is insight, not numbers," is particularly apt in the area of finding roots. You should repeat this motto aloud whenever your program converges, with ten-digit accuracy, to the wrong root of a problem, or whenever it fails to converge because there is actually no root, or because there is a root but your initial estimate was not sufficiently close to it.
"This talk of insight is all very well, but what do I actually do?" For one-dimensional root finding, it is possible to give some straightforward answers: You should try to get some idea of what your function looks like before trying to find its roots. If you need to mass-produce roots for many different functions, then you should at least know what some typical members of the ensemble look like. Next, you should always bracket a root, that is, know that the function changes sign in an identified interval, before trying to converge to the root's value.
Finally (this is advice with which some daring souls might disagree, but we give it nonetheless) never let your iteration method get outside of the best bracketing bounds obtained at any stage. We will see below that some pedagogically important algorithms, such as secant method or Newton-Raphson, can violate this last constraint, and are thus not recommended unless certain fixups are implemented.
Multiple roots, or very close roots, are a real problem, especially if the multiplicity is an even number. In that case, there may be no readily apparent sign change in the function, so the notion of bracketing a root -- and maintaining the bracket -- becomes difficult. We are hard-liners: we nevertheless insist on bracketing a root, even if it takes the minimum-searching techniques of Chapter 10 to determine whether a tantalizing dip in the function really does cross zero or not. (You can easily modify the simple golden section routine of §10.1 to return early if it detects a sign change in the function. And, if the minimum of the function is exactly zero, then you have found a double root.)
As usual, we want to discourage you from using routines as black boxes without understanding them. However, as a guide to beginners, here are some reasonable starting points:
Avoiding implementations for specific computers, this book must generally steer clear of interactive or graphics-related routines. We make an exception right now. The following routine, which produces a crude function plot with interactively scaled axes, can save you a lot of grief as you enter the world of root finding.
#include < stdio.h >
#define ISCR 60 Number of horizontal and vertical positions in display.
#define JSCR 21
#define BLANK ' '
#define ZERO '-'
#define YY 'l'
#define XX '-'
#define FF 'x'
void scrsho(float (*fx)(float))
For interactive CRT terminal use. Produce a crude graph of the function fx over the prompted-for interval x1, x2. Query for another plot until the user signals satisfaction.
|Preface to the Second Edition|
|Preface to the First Edition|
|Computer Programs by Chapter and Section|
|2||Solution of Linear Algebraic Equations||32|
|3||Interpolation and Extrapolation||105|
|4||Integration of Functions||129|
|5||Evaluation of Functions||165|
|9||Root Finding and Nonlinear Sets of Equations||347|
|10||Minimization or Maximization of Functions||394|
|12||Fast Fourier Transform||496|
|13||Fourier and Spectral Applications||537|
|14||Statistical Description of Data||609|
|15||Modeling of Data||656|
|16||Integration of Ordinary Differential Equations||707|
|17||Two Point Boundary Value Problems||753|
|18||Integral Equations and Inverse Theory||788|
|19||Partial Differential Equations||827|
|Appendix A: Table of Prototype Declarations||930|
|Appendix B: Utility Routines||940|
|Appendix C: Complex Arithmetic||948|
|Index of Programs and Dependencies||951|