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Chapter 9.

Root Finding and Nonlinear Sets of Equations

} a=b; fa=fb; if (fabs(d) > tol1) b += d; else b += SIGN(tol1,xm); fb=(*func)(b);

Move last best guess to a. Evaluate new trial root.

Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America).

} nrerror("Maximum number of iterations exceeded in zbrent"); return 0.0; Never get here. }

CITED REFERENCES AND FURTHER READING: Brent, R.P. 1973, Algorithms for Minimization without Derivatives (Englewood Cliffs, NJ: PrenticeHall), Chapters 3, 4. [1] Forsythe, G.E., Malcolm, M.A., and Moler, C.B. 1977, Computer Methods for Mathematical Computations (Englewood Cliffs, NJ: Prentice-Hall), §7.2.

9.4 Newton-Raphson Method Using Derivative

Perhaps the most celebrated of all one-dimensional root-ﬁnding routines is Newton’s method, also called the Newton-Raphson method. This method is distinguished from the methods of previous sections by the fact that it requires the evaluation of both the function f (x), and the derivative f (x), at arbitrary points x. The Newton-Raphson formula consists geometrically of extending the tangent line at a current point x i until it crosses zero, then setting the next guess x i+1 to the abscissa of that zero-crossing (see Figure 9.4.1). Algebraically, the method derives from the familiar Taylor series expansion of a function in the neighborhood of a point, f (x + δ) ≈ f (x) + f (x)δ + f (x) 2 δ + .... 2 (9.4.1)

For small enough values of δ, and for well-behaved functions, the terms beyond linear are unimportant, hence f (x + δ) = 0 implies δ=− f (x) . f (x) (9.4.2)

Newton-Raphson is not restricted to one dimension. The method readily generalizes to multiple dimensions, as we shall see in §9.6 and §9.7, below. Far from a root, where the higher-order terms in the series are important, the Newton-Raphson formula can give grossly inaccurate, meaningless corrections. For instance, the initial guess for the root might be so far from the true root as to let the search interval include a local maximum or minimum of the function. This can be death to the method (see Figure 9.4.2). If an iteration places a trial guess near such a local extremum, so that the ﬁrst derivative nearly vanishes, then NewtonRaphson sends its solution off to limbo, with vanishingly small hope of recovery.

Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America).

Figure 9.4.1. Newton’s method extrapolates the local derivative to ﬁnd the next estimate of the root. In this example it works well and converges quadratically.

Figure 9.4.2. Unfortunate case where Newton’s method encounters a local extremum and shoots off to outer space. Here bracketing bounds, as in rtsafe, would save the day.

363

1

x

9.4 Newton-Raphson Method Using Derivative

2

3

f (x)

f(x)

3

2

1

x

364

Chapter 9.

Root Finding and Nonlinear Sets of Equations

f (x)

1

Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America).

x

2

Figure 9.4.3. Unfortunate case where Newton’s method enters a nonconvergent cycle. This behavior is often encountered when the function f is obtained, in whole or in part, by table interpolation. With a better initial guess, the method would have succeeded.

Like most powerful tools, Newton-Raphson can be destructive used in inappropriate circumstances. Figure 9.4.3 demonstrates another possible pathology. Why do we call Newton-Raphson powerful? The answer lies in its rate of convergence: Within a small distance of x the function and its derivative are approximately: f (x + ) = f (x) + f (x) +

2f

f (x + ) = f (x) + f (x) + · · · By the Newton-Raphson formula, xi+1 = xi − so that i+1 (x) + ···, 2

(9.4.3)

f (xi ) , f (xi ) f (xi ) . f (xi )

(9.4.4)

=

i

−

(9.4.5)

When a trial solution xi differs from the true root by i , we can use (9.4.3) to express f (xi ), f (xi ) in (9.4.4) in terms of i and derivatives at the root itself. The result is a recurrence relation for the deviations of the trial solutions i+1 =−

2 i

f (x) . 2f (x)

(9.4.6)

9.4 Newton-Raphson Method Using Derivative

365

Equation (9.4.6) says that Newton-Raphson converges quadratically (cf. equation 9.2.3). Near a root, the number of signiﬁcant digits approximately doubles with each step. This very strong convergence property makes Newton-Raphson the method of choice for any function whose derivative can be evaluated efﬁciently, and whose derivative is continuous and nonzero in the neighborhood of a root. Even where Newton-Raphson is rejected for the early stages of convergence (because of its poor global convergence properties), it is very common to “polish up” a root with one or two steps of Newton-Raphson, which can multiply by two or four its number of signiﬁcant ﬁgures! For an efﬁcient realization of Newton-Raphson the user provides a routine that evaluates both f (x) and its ﬁrst derivative f (x) at the point x. The Newton-Raphson formula can also be applied using a numerical difference to approximate the true local derivative, f (x) ≈ f (x + dx) − f (x) . dx (9.4.7)

Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America).

This is not, however, a recommended procedure for the following reasons: (i) You are doing two function evaluations per step, so at best the superlinear order of √ convergence will be only 2. (ii) If you take dx too small you will be wiped out by roundoff, while if you take it too large your order of convergence will be only linear, no better than using the initial evaluation f (x0 ) for all subsequent steps. Therefore, Newton-Raphson with numerical derivatives is (in one dimension) always dominated by the secant method of §9.2. (In multidimensions, where there is a paucity of available methods, Newton-Raphson with numerical derivatives must be taken more seriously. See §§9.6–9.7.) The following function calls a user supplied function funcd(x,fn,df) which supplies the function value as fn and the derivative as df. We have included input bounds on the root simply to be consistent with previous root-ﬁnding routines: Newton does not adjust bounds, and works only on local information at the point x. The bounds are used only to pick the midpoint as the ﬁrst guess, and to reject the solution if it wanders outside of the bounds.

#include #define JMAX 20 Set to maximum number of iterations.

float rtnewt(void (*funcd)(float, float *, float *), float x1, float x2, float xacc) Using the Newton-Raphson method, ﬁnd the root of a function known to lie in the interval [x1, x2]. The root rtnewt will be reﬁned until its accuracy is known within ±xacc. funcd is a user-supplied routine that returns both the function value and the ﬁrst derivative of the function at the point x. { void nrerror(char error_text[]); int j; float df,dx,f,rtn; rtn=0.5*(x1+x2); Initial guess. for (j=1;j 0.0 && fh > 0.0) || (fl < 0.0 && fh < 0.0)) nrerror("Root must be bracketed in rtsafe"); if (fl == 0.0) return x1; if (fh == 0.0) return x2; if (fl < 0.0) { Orient the search so that f (xl) < 0. xl=x1; xh=x2; } else { xh=x1; xl=x2; } rts=0.5*(x1+x2); Initialize the guess for root, dxold=fabs(x2-x1); the “stepsize before last,” dx=dxold; and the last step. (*funcd)(rts,&f,&df); for (j=1;j 0.0) Bisect if Newton out of range, || (fabs(2.0*f) > fabs(dxold*df))) { or not decreasing fast enough. dxold=dx; dx=0.5*(xh-xl); rts=xl+dx; Change in root is negligible. if (xl == rts) return rts; } else { Newton step acceptable. Take it. dxold=dx; dx=f/df; temp=rts; rts -= dx; if (temp == rts) return rts; } if (fabs(dx) < xacc) return rts; Convergence criterion. (*funcd)(rts,&f,&df); The one new function evaluation per iteration.

9.4 Newton-Raphson Method Using Derivative

367

if (f < 0.0) xl=rts; else xh=rts;

Maintain the bracket on the root.

} nrerror("Maximum number of iterations exceeded in rtsafe"); return 0.0; Never get here. }

Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America).

For many functions the derivative f (x) often converges to machine accuracy before the function f (x) itself does. When that is the case one need not subsequently update f (x). This shortcut is recommended only when you conﬁdently understand the generic behavior of your function, but it speeds computations when the derivative calculation is laborious. (Formally this makes the convergence only linear, but if the derivative isn’t changing anyway, you can do no better.)

Newton-Raphson and Fractals

An interesting sidelight to our repeated warnings about Newton-Raphson’s unpredictable global convergence properties — its very rapid local convergence notwithstanding — is to investigate, for some particular equation, the set of starting values from which the method does, or doesn’t converge to a root. Consider the simple equation z3 − 1 = 0 (9.4.8)

whose single real root is z = 1, but which also has complex roots at the other two cube roots of unity, exp(±2πi/3). Newton’s method gives the iteration zj+1 = zj −

3 zj − 1 2 3zj

(9.4.9)

Up to now, we have applied an iteration like equation (9.4.9) only for real starting values z0 , but in fact all of the equations in this section also apply in the complex plane. We can therefore map out the complex plane into regions from which a starting value z 0 , iterated in equation (9.4.9), will, or won’t, converge to z = 1. Naively, we might expect to ﬁnd a “basin of convergence” somehow surrounding the root z = 1. We surely do not expect the basin of convergence to ﬁll the whole plane, because the plane must also contain regions that converge to each of the two complex roots. In fact, by symmetry, the three regions must have identical shapes. Perhaps they will be three symmetric 120 ◦ wedges, with one root centered in each? Now take a look at Figure 9.4.4, which shows the result of a numerical exploration. The basin of convergence does indeed cover 1/3 the area of the complex plane, but its boundary is highly irregular — in fact, fractal. (A fractal, so called, has self-similar structure that repeats on all scales of magniﬁcation.) How does this fractal emerge from something as simple as Newton’s method, and an equation as simple as (9.4.8)? The answer is already implicit in Figure 9.4.2, which showed how, on the real line, a local extremum causes Newton’s method to shoot off to inﬁnity. Suppose one is slightly removed from such a point. Then one might be shot off not to inﬁnity, but — by luck — right into the basin of convergence of the desired

368

Chapter 9.

Root Finding and Nonlinear Sets of Equations

Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America).

Figure 9.4.4. The complex z plane with real and imaginary components in the range (−2, 2). The black region is the set of points from which Newton’s method converges to the root z = 1 of the equation z 3 − 1 = 0. Its shape is fractal.

root. But that means that in the neighborhood of an extremum there must be a tiny, perhaps distorted, copy of the basin of convergence — a kind of “one-bounce away” copy. Similar logic shows that there can be “two-bounce” copies, “three-bounce” copies, and so on. A fractal thus emerges. Notice that, for equation (9.4.8), almost the whole real axis is in the domain of convergence for the root z = 1. We say “almost” because of the peculiar discrete points on the negative real axis whose convergence is indeterminate (see ﬁgure). What happens if you start Newton’s method from one of these points? (Try it.)

CITED REFERENCES AND FURTHER READING: Acton, F.S. 1970, Numerical Methods That Work; 1990, corrected edition (Washington: Mathematical Association of America), Chapter 2. Ralston, A., and Rabinowitz, P. 1978, A First Course in Numerical Analysis, 2nd ed. (New York: McGraw-Hill), §8.4. Ortega, J., and Rheinboldt, W. 1970, Iterative Solution of Nonlinear Equations in Several Variables (New York: Academic Press). Mandelbrot, B.B. 1983, The Fractal Geometry of Nature (San Francisco: W.H. Freeman). Peitgen, H.-O., and Saupe, D. (eds.) 1988, The Science of Fractal Images (New York: SpringerVerlag).

9.5 Roots of Polynomials

369

9.5 Roots of Polynomials

Here we present a few methods for ﬁnding roots of polynomials. These will serve for most practical problems involving polynomials of low-to-moderate degree or for well-conditioned polynomials of higher degree. Not as well appreciated as it ought to be is the fact that some polynomials are exceedingly ill-conditioned. The tiniest changes in a polynomial’s coefﬁcients can, in the worst case, send its roots sprawling all over the complex plane. (An infamous example due to Wilkinson is detailed by Acton [1].) Recall that a polynomial of degree n will have n roots. The roots can be real or complex, and they might not be distinct. If the coefﬁcients of the polynomial are real, then complex roots will occur in pairs that are conjugate, i.e., if x 1 = a + bi is a root then x2 = a − bi will also be a root. When the coefﬁcients are complex, the complex roots need not be related. Multiple roots, or closely spaced roots, produce the most difﬁculty for numerical algorithms (see Figure 9.5.1). For example, P (x) = (x − a) 2 has a double real root at x = a. However, we cannot bracket the root by the usual technique of identifying neighborhoods where the function changes sign, nor will slope-following methods such as Newton-Raphson work well, because both the function and its derivative vanish at a multiple root. Newton-Raphson may work, but slowly, since large roundoff errors can occur. When a root is known in advance to be multiple, then special methods of attack are readily devised. Problems arise when (as is generally the case) we do not know in advance what pathology a root will display.

Sample page from NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43108-5) Copyright (C) 1988-1992 by Cambridge University Press. Programs Copyright (C) 1988-1992 by Numerical Recipes Software. Permission is granted for internet users to make one paper copy for their own personal use. Further reproduction, or any copying of machinereadable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, visit website http://www.nr.com or call 1-800-872-7423 (North America only), or send email to directcustserv@cambridge.org (outside North America).

Deﬂation of Polynomials

When seeking several or all roots of a polynomial, the total effort can be signiﬁcantly reduced by the use of deﬂation. As each root r is found, the polynomial is factored into a product involving the root and a reduced polynomial of degree one less than the original, i.e., P (x) = (x − r)Q(x). Since the roots of Q are exactly the remaining roots of P , the effort of ﬁnding additional roots decreases, because we work with polynomials of lower and lower degree as we ﬁnd successive roots. Even more important, with deﬂation we can avoid the blunder of having our iterative method converge twice to the same (nonmultiple) root instead of separately to two different roots. Deﬂation, which amounts to synthetic division, is a simple operation that acts on the array of polynomial coefﬁcients. The concise code for synthetic division by a monomial factor was given in §5.3 above. You can deﬂate complex roots either by converting that code to complex data type, or else — in the case of a polynomial with real coefﬁcients but possibly complex roots — by deﬂating by a quadratic factor, [x − (a + ib)] [x − (a − ib)] = x2 − 2ax + (a2 + b2 ) (9.5.1)

The routine poldiv in §5.3 can be used to divide the polynomial by this factor. Deﬂation must, however, be utilized with care. Because each new root is known with only ﬁnite accuracy, errors creep into the determination of the coefﬁcients of the successively deﬂated polynomial. Consequently, the roots can become more and more inaccurate. It matters a lot whether the inaccuracy creeps in stably (plus or…...

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...It may also attract investors that prefer companies that pay dividends. For example, a cash dividend is to be paid at specified times (usually quarterly), however a stock repurchase is not. For some investors, the dependability of the dividend may be more important. Also, I think that dividends help to avoid wasting firms’ cash on not necessarily needed or unproductive projects or acquisitions. 3. Linear Technology is considering increasing dividends in the 4th quarter of 2003. Is Linear Technology in a financial position to consider a dividend increase (particularly in light of the sharp decline in sales and profits in fiscal year 2002)? It seems to me that Linear Technology is not in an appropriate financial position to consider a dividend increase. The company`s performance was good, but numbers were far below the levels of fiscal year 2001. Case study claims that “management did not see a clear path of reaching levels of 2001 in the next years” (Baker, 2004). Moreover, there were some geopolitical factors (war in Iraq) that challenged the US economy. 4. Suppose Linear Technology determines it wants to return more cash to shareholders. One way to do this is through increasing its regular quarterly dividend payment to shareholders. Name another way to distribute cash to shareholders other than dividends. What are the advantages and disadvantages of this alternative way of distributing cash? Another way to distribute cash to shareholders is stock repurchase.......

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... One of the most important concepts that we learned in this course is formulating linear equations from everyday life problems that need solutions. With this concept under your belt you will always be able to find solutions in everday life. For example what if you have a new house interior to paint and you need to figure out how much paint you should purchase. Through this course we have attained the ability to determine the exact amount of paint needed to be purchased. What many fail to realize is that math is in our lives daily on multiple occasions. This course provided the comfort of being able to handle this daily math without worries. Out of all the concepts explained inequalities seem to be the least important to everyday life. Although they seem the least important does not mean they are useless. Someone somewhere is using these equations for a significant task. How do you think you will use the information you learned in this course in the future? Which concepts will be most important to you? Which will be least important? Explain your answers. I will continue to use basic mathematical (algebraic) calculations such as expenses versus income in applicable situations, estimation of materials based upon linear measurements and the calculation of expenses based upon the cost factor of those materials as I progress in my personal life. I do see myself for personal reasons, using simple graphs to get ideas or guidance on how my personal venture(s)......

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...Merton Trucks Case Note Abstract We discuss Merton Trucks [Dhe90a] as a case to introduce linear programming in the MBA program. This case adapted from Sherman Motor Company case, was used to introduce Linear Programming formulations as well as duality. Refer to the teaching note [Dhe90b]. Our approach differs from the approach suggested by Dhebar [Dhe90b]. First, our audience consists pre-dominantly of engineers with not too much work experience. As a result, handling math and algebra is relatively easy. Explaining the algebraic formulation, graphical approach and using the Excel solver do not consume that much time. Second, because this case is used during the ﬁrst week of the MBA program, students are still unfamiliar with the case methodology and we spend signiﬁcant time in understanding case facts. The circular logic used in allocating ﬁxed costs based on the product mix that in turn is used in deciding the product mix takes some time to understand. Third, because of the participant background, they have difﬁculty in translating the model to the speciﬁc business situation and interpreting the trade-offs involved in various what-if analyses that are prompted by the case questions. We return to the case when we teach duality. After explaining duality, we analyze the case to show how some of the questions and what-if analyses can be simpliﬁed using duality. This note is based on our experiences with teaching three large batches of students in our MBA programs. 1 1......

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...(in connection with the planning activities of the military), linear programming and its many extensions have come into wide use. In academic circles decision scientists (operations researchers and management scientists), as well as numerical analysts, mathematicians, and economists have written hundreds of books and an uncountable number of articles on the subject. Curiously, in spite of its wide applicability today to everyday problems, it was unknown prior to 1947. This is not quite correct; there were some isolated exceptions. Fourier (of Fourier series fame) in 1823 and the wellknown Belgian mathematician de la Vallée Poussin in 1911 each wrote a paper about it, but that was about it. Their work had as much inﬂuence on Post-1947 developments as would ﬁnding in an Egyptian tomb an electronic computer built in 3000 BC. Leonid Kantorovich’s remarkable 1939 monograph on the subject was also neglected for ideological reasons in the USSR. It was resurrected two decades later after the major developments had already taken place in the West. An excellent paper by Hitchcock in 1941 on the transportation problem was also overlooked until after others in the late 1940’s and early 1950’s had independently rediscovered its properties. What seems to characterize the pre-1947 era was lack of any interest in trying to optimize. T. Motzkin in his scholarly thesis written in 1936 cites only 42 papers on linear inequality systems, none of which mentioned an......

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...ALISON BERKLEY WAGONFELD Dividend Policy at Linear Technology It was April 2003 and Paul Coghlan was pulling together his notes for Linear Technology’s board meeting the following day. As chief financial officer of the Silicon Valley semiconductor company, Coghlan was responsible for making a recommendation about whether or not Linear should increase its dividend this quarter. Coghlan and Linear’s CEO Robert Swanson were pleased with the company’s third-quarter financials for fiscal year 2003, but sales and net income still remained substantially below Linear’s record levels set in 2001. In addition, the technology industry was still emerging from a recessionary environment and it was unclear how strong business would be for the remainder of the year. Linear Technology Corporation Headquartered in Milpitas, California, Linear was founded in 1981 by Robert Swanson. Under his leadership, the company focused on designing, manufacturing, and marketing integrated circuits (semiconductors) that were used in various electronic applications such as cellular telephones, digital cameras, complex medical devices, and navigation systems. Linear’s customers spanned numerous industries and no single customer accounted for more than 5% of its business. In 2002, the communications industry accounted for 33% of Linear’s business, computers 27%, automotive 6%, and the remaining 34% was spread across many different applications. Linear focused on the analog segment within......

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