# Convergence of finite differences¶

All of the finite difference formulas in the previous section based on equally spaced nodes converge as the node spacing \(h\) decreases to zero. However, note that to discretize a function over an interval \([a,b]\), we use \(h=(b-a)/n\), which implies \(n=(b-a)/h=O(h^{-1})\). As \(h\to 0\), the total number of nodes needed grows without bound. So we would like to make \(h\) as large as possible while still achieving some acceptable accuracy.

To measure this kind of performance, we introduce the truncation error of a finite difference formula, defined as the difference between the two sides of (131):

where we have used translation invariance to set \(x=0\) for simplicity. In order to make this expression useful, we expand it in a Taylor series about \(h=0\) and ignore all but the leading terms.1

The most important conclusion of (142) is that \(\tau_f(h)=O(h)\). The dependence on \(h\) raised to the first power leads us to call this a **first-order accurate** formula. In a first-order formula, cutting \(h\) in half should reduce the error in the \(f'\) estimate by about half as well, when \(h\) is small enough.

## Higher-order accuracy¶

As a rule, including more function values in a finite difference formula (i.e., increasing \(p\) and \(q\) in (131)) gives a truncation error that depends on a higher power of \(h\) and thus vanishes more quickly as \(h\to 0\). The power of \(h\) in the leading term of the truncation error is known as the order of accuracy.

We compute the truncation error of (135):

Because the lowest-order term is proportional to \(h^2\), we say that the method has order of accuracy equal to 2.

Equation (143) implies that (135) is second-order accurate. Reducing \(h\) by a factor of two should cut the error in the \(f'\) estimate by a factor of roughly four. This convergence is much more rapid than for the first-order formula (132).

## Stability¶

The truncation error \(\tau_f(h)\) of a finite difference formula is dominated by a leading term \(O(h^m)\) for an integer \(m\). This error decreases as \(h\to 0\). However, we have not yet accounted for the effects of roundoff error. To keep matters as simple as possible, let’s consider the forward difference

As \(h\to 0\), the numerator approaches zero even though the values \(f(x+h)\) and \(f(x)\) are not necessarily near zero. This is the recipe for subtractive cancellation error! In fact, finite difference formulas are inherently ill-conditioned as \(h\to 0\) when evaluated in floating point arithmetic.

To be precise, recall that the condition number for the problem of computing \(f(x+h)-f(x)\) is

implying a relative error of size \(\kappa(h) \epsilon_\text{mach}\) in its computation. Hence the numerical value we actually compute for \(\delta\) is

As \(h\to 0\), \(f(x+h)=f(x)+O(h)\), so we can simplify the maximization in the expression to get

Hence as \(h \to 0\), the roundoff error is \(O(h^{-1})\), which grows without bound. Combining truncation error with roundoff error leads to

Equation (144) quantifies the contributions of both truncation and roundoff errors. While the truncation error \(\tau\) vanishes as \(h\) decreases, the roundoff error actually *increases* thanks to the subtractive cancellation. At some value of \(h\) the two error contributions will be of roughly equal size. This occurs roughly when

for a constant \(K\) that depends on \(x\) and \(f\), but not \(h\). For a method of truncation order \(m\), the details of the subtractive cancellation are a bit different, but the conclusion generalizes to

Finally, at the optimal \(h\) from (145), both the truncation and roundoff errors are

Hence for a first order formula (\(m=1\)), we can expect only \(O\left(\sqrt{\epsilon_\text{mach}}\right)\) error, or about half of the number of accurate machine digits. As \(m\) increases, we get ever closer to using the full accuracy available.

The observations in Roundoff in finite differences match the analysis above quite well, with the optimal \(h\) becoming larger and the optimal error getting smaller as the order increases. Thus, higher-order finite difference methods are both more efficient and less vulnerable to roundoff than low-order methods.

## Exercises¶

⌨ Write a code to evaluate the centered 2nd-order finite difference approximation to \(f'(\pi/7)\) for \(f(x)=\cos(x)\) and \(h=2^{-1},2^{-2},\ldots,2^{-7}\). On a log–log graph, plot the error as a function of \(h\) and compare it graphically to second-order convergence.

✍ Derive the first two nonzero terms of the Taylor series at \(h=0\) of the truncation error \(\tau_{f}(h)\) for the formula (133).

✍ Calculate the first nonzero term in the Taylor series of the truncation error \(\tau_{f}(h)\) for the finite difference formula defined by the second row of~Weights for forward finite difference formulas..

✍ Calculate the first nonzero term in the Taylor series of the truncation error \(\tau_{f}(h)\) for the finite difference formula defined by the third row of~Weights for forward finite difference formulas..

✍ Using natural extensions of our definitions, show that the formula (137) is second-order accurate.

✍ A different way to derive finite difference formulas is the

**method of undetermined coefficients**. Starting from (131),\[f'(x) \approx \frac{1}{h}\sum_{k=-p}^q a_k f(x+kh),\]let each \(f(x+k h)\) be expanded in a series around \(h=0\). When the coefficients of powers of \(h\) are collected, one obtains

\[\frac{1}{h} \sum_{k=-p}^q a_k f(x+kh) = \frac{b_0}{h} + b_1 f'(x) + b_2 f''(x)h + \cdots,\]where

\[b_i = \sum_{k=-p}^q k^i a_k.\]In order to make the result as close as possible to \(f'(x)\), we impose the conditions

\[b_0 = 0,\, b_1=1,\, b_2=0,\, b_3=0,\,\ldots,\,b_{p+q}=0.\]This provides a system of linear equations for the weights.

**(a)**For \(p=q=2\), write out the system of equations for \(a_{-2}\), \(a_{-1}\), \(a_0\), \(a_1\), \(a_2\).**(b)**Verify that the coefficients from the appropriate row of Table 2 satisfy the equations you wrote down in part (a).**(c)**Derive the finite difference formula for \(p=1\), \(q=2\) using the method of undetermined coefficients.

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The term

*truncation error*is derived from the idea that the finite difference formula, being finite, has to truncate the series representation and thus cannot be exactly correct for all functions.