# 5 Numerical Differentiation

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1 D. Levy 5 Numerical Differentiation 5. Basic Concepts This chapter deals with numerical approximations of derivatives. The first questions that comes up to mind is: why do we need to approximate derivatives at all? After all, we do know how to analytically differentiate every function. Nevertheless, there are several reasons as of why we still need to approximate derivatives: Even if there exists an underlying function that we need to differentiate, we might know its values only at a sampled data set without knowing the function itself. There are some cases where it may not be obvious that an underlying function exists and all that we have is a discrete data set. We may still be interested in studying changes in the data, which are related, of course, to derivatives. There are times in which exact formulas are available but they are very complicated to the point that an exact computation of the derivative requires a lot of function evaluations. It might be significantly simpler to approximate the derivative instead of computing its exact value. When approximating solutions to ordinary (or partial) differential equations, we typically represent the solution as a discrete approximation that is defined on a grid. Since we then have to evaluate derivatives at the grid points, we need to be able to come up with methods for approximating the derivatives at these points, and again, this will typically be done using only values that are defined on a lattice. The underlying function itself (which in this cased is the solution of the equation) is unknown. A simple approximation of the first derivative is f (x) f(x + h) f(x), (5.) h where we assume that h > 0. What do we mean when we say that the expression on the right-hand-side of (5.) is an approximation of the derivative? For linear functions (5.) is actually an exact expression for the derivative. For almost all other functions, (5.) is not the exact derivative. Let s compute the approximation error. We write a Taylor expansion of f(x + h) about x, i.e., f(x + h) = f(x) + hf (x) + h2 2 f (ξ), ξ (x, x + h). (5.2) For such an expansion to be valid, we assume that f(x) has two continuous derivatives. The Taylor expansion (5.2) means that we can now replace the approximation (5.) with

2 5. Basic Concepts D. Levy an exact formula of the form f(x + h) f(x) h h 2 f (ξ), ξ (x, x + h). (5.3) Since this approximation of the derivative at x is based on the values of the function at x and x + h, the approximation (5.) is called a forward differencing or one-sided differencing. The approximation of the derivative at x that is based on the values of the function at x h and x, i.e., f (x) f(x) f(x h), h is called a backward differencing (which is obviously also a one-sided differencing formula). The second term on the right-hand-side of (5.3) is the error term. Since the approximation (5.) can be thought of as being obtained by truncating this term from the exact formula (5.3), this error is called the truncation error. The small parameter h denotes the distance between the two points x and x+h. As this distance tends to zero, i.e., h 0, the two points approach each other and we expect the approximation (5.) to improve. This is indeed the case if the truncation error goes to zero, which in turn is the case if f (ξ) is well defined in the interval (x, x + h). The speed in which the error goes to zero as h 0 is called the rate of convergence. When the truncation error is of the order of O(h), we say that the method is a first order method. We refer to a methods as a p th -order method if the truncation error is of the order of O(h p ). It is possible to write more accurate formulas than (5.3) for the first derivative. For example, a more accurate approximation for the first derivative that is based on the values of the function at the points f(x h) and f(x + h) is the centered differencing formula f (x). (5.4) Let s verify that this is indeed a more accurate formula than (5.). Taylor expansions of the terms on the right-hand-side of (5.4) are f(x + h) = f(x) + hf (x) + h2 2 f (x) + h3 6 f (ξ ), f(x h) = f(x) hf (x) + h2 2 f (x) h3 6 f (ξ 2 ). Here ξ (x, x + h) and ξ 2 (x h, x). Hence 2 [f (ξ ) + f (ξ 2 )], 2

3 D. Levy 5.2 Differentiation Via Interpolation which means that the truncation error in the approximation (5.4) is 2 [f (ξ ) + f (ξ 2 )]. If the third-order derivative f (x) is a continuous function in the interval [x h, x + h], then the intermediate value theorem implies that there exists a point ξ (x h, x + h) such that Hence f (ξ) = 2 [f (ξ ) + f (ξ 2 )]. 6 f (ξ), (5.5) which means that the expression (5.4) is a second-order approximation of the first derivative. In a similar way we can approximate the values of higher-order derivatives. For example, it is easy to verify that the following is a second-order approximation of the second derivative f (x) f(x + h) 2f(x) + f(x h) h 2. (5.6) To verify the consistency and the order of approximation of (5.6) we expand f(x ± h) = f(x) ± hf (x) + h2 2 f (x) ± h3 6 f (x) + h4 24 f (4) (ξ ± ). Here, ξ (x h, x) and ξ + (x, x + h). Hence f(x + h) 2f(x) + f(x h) h 2 = f (x)+ h2 ( f (4) (ξ ) + f (4) (ξ + ) ) = f (x)+ h f (4) (ξ), where we assume that ξ (x h, x + h) and that f(x) has four continuous derivatives in the interval. Hence, the approximation (5.6) is indeed a second-order approximation of the derivative, with a truncation error that is given by 2 f (4) (ξ), ξ (x h, x + h). 5.2 Differentiation Via Interpolation In this section we demonstrate how to generate differentiation formulas by differentiating an interpolant. The idea is straightforward: the first stage is to construct an interpolating polynomial from the data. An approximation of the derivative at any point can be then obtained by a direct differentiation of the interpolant. 3

4 5.2 Differentiation Via Interpolation D. Levy We follow this procedure and assume that f(x 0 ),..., f(x n ) are given. The Lagrange form of the interpolation polynomial through these points is Q n (x) = f(x j )l j (x). Here we simplify the notation and replace li n (x) which is the notation we used in Section?? by l i (x). According to the error analysis of Section?? we know that the interpolation error is f(x) Q n (x) = (n + )! f (n+) (ξ n ) n (x x j ), where ξ n (min(x, x 0,..., x n ), max(x, x 0,..., x n )). Since here we are assuming that the points x 0,..., x n are fixed, we would like to emphasize the dependence of ξ n on x and hence replace the ξ n notation by ξ x. We that have: f(x) = f(x j )l j (x) + (n + )! f (n+) (ξ x )w(x), (5.7) where w(x) = n (x x i ). i=0 Differentiating the interpolant (5.7): f(x j )l j(x) + (n + )! f (n+) (ξ x )w (x) + (n + )! w(x) d dx f (n+) (ξ x ). (5.8) We now assume that x is one of the interpolation points, i.e., x {x 0,..., x n }, say x k, so that f (x k ) = f(x j )l j(x k ) + (n + )! f (n+) (ξ xk )w (x k ). (5.9) Now, w (x) = i=0 n (x x j ) = j i [(x x 0 )... (x x i )(x x i+ )... (x x n )]. i=0 Hence, when w (x) is evaluated at an interpolation point x k, there is only one term in w (x) that does not vanish, i.e., n w (x k ) = (x k x j ). j k 4

5 D. Levy 5.2 Differentiation Via Interpolation The numerical differentiation formula, (5.9), then becomes f (x k ) = f(x j )l j(x k ) + (n + )! f (n+) (ξ xk ) j k (x k x j ). (5.0) We refer to the formula (5.0) as a differentiation by interpolation algorithm. Example 5. We demonstrate how to use the differentiation by integration formula (5.0) in the case where n = and k = 0. This means that we use two interpolation points (x 0, f(x 0 )) and (x, f(x )), and want to approximate f (x 0 ). The Lagrange interpolation polynomial in this case is where Hence Q (x) = f(x 0 )l 0 (x) + f(x )l (x), l 0 (x) = x x x 0 x, l (x) = x x 0 x x 0. l 0(x) = We thus have x 0 x, l (x) = x x 0. Q (x 0 ) = f(x 0) x 0 x + f(x ) x x f (ξ)(x 0 x ) = f(x ) f(x 0 ) x x 0 2 f (ξ)(x x 0 ). Here, we simplify the notation and assume that ξ (x 0, x ). If we now let x = x 0 + h, then f (x 0 ) = f(x 0 + h) f(x 0 ) h h 2 f (ξ), which is the (first-order) forward differencing approximation of f (x 0 ), (5.3). Example 5.2 We repeat the previous example in the case n = 2 and k = 0. This time with Q 2 (x) = f(x 0 )l 0 (x) + f(x )l (x) + f(x 2 )l 2 (x), l 0 (x) = (x x )(x x 2 ) (x 0 x )(x 0 x 2 ), l (x) = (x x 0)(x x 2 ) (x x 0 )(x x 2 ), l 2(x) = (x x 0)(x x ) (x 2 x 0 )(x 2 x ). 5

6 5.3 The Method of Undetermined Coefficients D. Levy Hence l 0(x) = 2x x x 2 (x 0 x )(x 0 x 2 ), l (x) = Evaluating l j(x) for j =, 2, 3 at x 0 we have l 0(x 0 ) = 2x 0 x x 2 (x 0 x )(x 0 x 2 ), l (x 0 ) = Hence 2x x 0 x 2 (x x 0 )(x x 2 ), l 2(x) = x 0 x 2 (x x 0 )(x x 2 ), l 2(x 0 ) = 2x x 0 x (x 2 x 0 )(x 2 x ). x 0 x (x 2 x 0 )(x 2 x ) Q 2(x 0 ) = 2x 0 x x 2 f(x 0 ) (x 0 x )(x 0 x 2 ) + f(x x 0 x 2 ) (x x 0 )(x x 2 ) x 0 x +f(x 2 ) (x 2 x 0 )(x 2 x ) + 6 f (3) (ξ)(x 0 x )(x 0 x 2 ). (5.) Here, we assume ξ (x 0, x 2 ). For x i = x + ih, i = 0,, 2, equation (5.) becomes Q 2(x) = f(x) 3 + f(x + h) 2 ( + f(x + ) ) + f (ξ) h 2 h 3 3f(x) + 4f(x + h) f(x + ) = + f (ξ) h 2, 3 which is a one-sided, second-order approximation of the first derivative. Remark. In a similar way, if we were to repeat the last example with n = 2 while approximating the derivative at x, the resulting formula would be the second-order centered approximation of the first-derivative (5.5) 6 f (ξ)h The Method of Undetermined Coefficients In this section we present the method of undetermined coefficients, which is a very practical way for generating approximations of derivatives (as well as other quantities as we shall see, e.g., when we discuss integration). Assume, for example, that we are interested in finding an approximation of the second derivative f (x) that is based on the values of the function at three equally spaced points, f(x h), f(x), f(x + h), i.e., f (x) Af(x + h) + B(x) + Cf(x h). (5.2) 6

7 D. Levy 5.3 The Method of Undetermined Coefficients The coefficients A, B, and C are to be determined in such a way that this linear combination is indeed an approximation of the second derivative. The Taylor expansions of the terms f(x ± h) are f(x ± h) = f(x) ± hf (x) + h2 2 f (x) ± h3 6 f (x) + h4 24 f (4) (ξ ± ), (5.3) where (assuming that h > 0) x h ξ x ξ + x + h. Using the expansions in (5.3) we can rewrite (5.2) as f (x) Af(x + h) + Bf(x) + Cf(x h) (5.4) = (A + B + C)f(x) + h(a C)f (x) + h2 2 (A + C)f (x) + h3 6 (A C)f (3) (x) + h4 24 [Af (4) (ξ + ) + Cf (4) (ξ )]. Equating the coefficients of f(x), f (x), and f (x) on both sides of (5.4) we obtain the linear system A + B + C = 0, A C = 0, A + C = 2 h 2. (5.5) The system (5.5) has the unique solution: A = C = h 2, B = 2 h 2. In this particular case, since A and C are equal to each other, the coefficient of f (3) (x) on the right-hand-side of (5.4) also vanishes and we end up with f (x) = f(x + h) 2f(x) + f(x h) h 2 24 [f (4) (ξ + ) + f (4) (ξ )]. We note that the last two terms can be combined into one using an intermediate values theorem (assuming that f(x) has four continuous derivatives), i.e., h 2 24 [f (4) (ξ + ) + f (4) (ξ )] = h2 2 f (4) (ξ), ξ (x h, x + h). Hence we obtain the familiar second-order approximation of the second derivative: f (x) = f(x + h) 2f(x) + f(x h) h 2 2 f (4) (ξ). In terms of an algorithm, the method of undetermined coefficients follows what was just demonstrated in the example: 7

9 D. Levy 5.4 Richardson s Extrapolation where L denotes the quantity that we are interested in approximating, i.e., L = f (x), and D(h) is the approximation, which in this case is D(h) = The error is. E = e 2 h 2 + e 4 h where e i denotes the coefficient of h i in (5.6). The important property of the coefficients e i s is that they do not depend on h. We note that the formula L D(h), is a second-order approximation of the first-derivative which is based on the values of f(x) at x ± h. We assume here that in general e i 0. In order to improve the approximation of L our strategy will be to eliminate the term e 2 h 2 from the error. How can this be done? one possibility is to write another approximation that is based on the values of the function at different points. For example, we can write L = D() + e 2 () 2 + e 4 () (5.8) This, of course, is still a second-order approximation of the derivative. However, the idea is to combine (5.7) with (5.8) such that the h 2 term in the error vanishes. Indeed, subtracting the following equations from each other 4L = 4D(h) + 4e 2 h 2 + 4e 4 h , L = D() + 4e 2 h 2 + 6e 4 h , we have L = 4D(h) D() 3 4e 4 h In other words, a fourth-order approximation of the derivative is f(x + ) + 8f(x + h) 8f(x h) + f(x ) + O(h 4 ). (5.9) Note that (5.9) improves the accuracy of the approximation (5.6) by using more points. 9

10 5.4 Richardson s Extrapolation D. Levy This process can be repeated over and over as long as the structure of the error is known. For example, we can write (5.9) as where L = S(h) + a 4 h 4 + a 6 h (5.20) S(h) = f(x + ) + 8f(x + h) 8f(x h) + f(x ). Equation (5.20) can be turned into a sixth-order approximation of the derivative by eliminating the term a 4 h 4. We carry out such a procedure by writing L = S() + a 4 () 4 + a 6 () (5.2) Combining (5.2) with (5.20) we end up with a sixth-order approximation of the derivative: L = Remarks. 6S(h) S() 5 + O(h 6 ).. In (5.8), instead of using D(), it is possible to use other approximations, e.g., D(h/2). If this is what is done, instead of (5.9) we would get a fourth-order approximation of the derivative that is based on the values of f at x h, x h/2, x + h/2, x + h. 2. Once again we would like to emphasize that Richardson s extrapolation is a general procedure for improving the accuracy of numerical approximations that can be used when the structure of the error is known. It is not specific for numerical differentiation. 0

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