Taylor series

As the degree of the Taylor polynomial rises, it approaches the correct function. This image shows Template:Math and its Taylor approximations, polynomials of degree 1, 3, 5, 7, 9, 11 and 13.

In mathematics, a Taylor series is a representation of a function as an infinite sum of terms that are calculated from the values of the function's derivatives at a single point.

In the West, the subject was formulated by the Scottish mathematician James Gregory and formally introduced by the English mathematician Brook Taylor in 1715. If the Taylor series is centered at zero, then that series is also called a Maclaurin series, after the Scottish mathematician Colin Maclaurin, who made extensive use of this special case of Taylor series in the 18th century.

A function can be approximated by using a finite number of terms of its Taylor series. Taylor's theorem gives quantitative estimates on the error introduced by the use of such an approximation. The polynomial formed by taking some initial terms of the Taylor series is called a Taylor polynomial. The Taylor series of a function is the limit of that function's Taylor polynomials as the degree increases, provided that the limit exists. A function may not be equal to its Taylor series, even if its Taylor series converges at every point. A function that is equal to its Taylor series in an open interval (or a disc in the complex plane) is known as an analytic function in that interval.

Definition

The Taylor series of a real or complex-valued function Template:Math that is infinitely differentiable at a real or complex number Template:Math is the power series

$f(a)+{\frac {f'(a)}{1!}}(x-a)+{\frac {f''(a)}{2!}}(x-a)^{2}+{\frac {f'''(a)}{3!}}(x-a)^{3}+\cdots ,$

where Template:Math denotes the factorial of Template:Mvar and Template:Math denotes the Template:Mvarth derivative of Template:Mvar evaluated at the point Template:Mvar. In the more compact sigma notation, this can be written as

$\sum _{n=0}^{\infty }{\frac {f^{(n)}(a)}{n!}}(x-a)^{n}.$

The derivative of order zero of Template:Mvar is defined to be Template:Mvar itself and Template:Math and Template:Math are both defined to be 1. When Template:Math, the series is also called a Maclaurin series.

Examples

The Taylor series for any polynomial is the polynomial itself.

The Maclaurin series for Template:Math is the geometric series

$1+x+x^{2}+x^{3}+\cdots$

so the Taylor series for Template:Math at Template:Math is

$1-(x-1)+(x-1)^{2}-(x-1)^{3}+\cdots .$

By integrating the above Maclaurin series, we find the Maclaurin series for Template:Math, where ln denotes the natural logarithm:

$-x-{\tfrac {1}{2}}x^{2}-{\tfrac {1}{3}}x^{3}-{\tfrac {1}{4}}x^{4}-\cdots$.

The corresponding Taylor series for Template:Math at Template:Math is

$(x-1)-{\tfrac {1}{2}}(x-1)^{2}+{\tfrac {1}{3}}(x-1)^{3}-{\tfrac {1}{4}}(x-1)^{4}+\cdots ,$

and more generally, the corresponding Taylor series for Template:Math at an arbitrary nonzero point Template:Mvar is:

$\ln a+{\frac {1}{a}}(x-a)-{\frac {1}{a^{2}}}{\frac {\left(x-a\right)^{2}}{2}}+\cdots .$

The Taylor series for the exponential function Template:Math at Template:Math is

${\begin{aligned}\sum _{n=0}^{\infty }{\frac {x^{n}}{n!}}&={\frac {x^{0}}{0!}}+{\frac {x^{1}}{1!}}+{\frac {x^{2}}{2!}}+{\frac {x^{3}}{3!}}+{\frac {x^{4}}{4!}}+{\frac {x^{5}}{5!}}+\cdots \\&=1+x+{\frac {x^{2}}{2}}+{\frac {x^{3}}{6}}+{\frac {x^{4}}{24}}+{\frac {x^{5}}{120}}+\cdots .\end{aligned}}$

The above expansion holds because the derivative of Template:Math with respect to Template:Mvar is also Template:Math and Template:Math equals 1. This leaves the terms Template:Math in the numerator and Template:Math in the denominator for each term in the infinite sum.

History

The Greek philosopher Zeno considered the problem of summing an infinite series to achieve a finite result, but rejected it as an impossibility; the result was Zeno's paradox. Later, Aristotle proposed a philosophical resolution of the paradox, but the mathematical content was apparently unresolved until taken up by Archimedes, as it had been prior to Aristotle by the Presocratic Atomist Democritus. It was through Archimedes's method of exhaustion that an infinite number of progressive subdivisions could be performed to achieve a finite result. Liu Hui independently employed a similar method a few centuries later.

In the 14th century, the earliest examples of the use of Taylor series and closely related methods were given by Madhava of Sangamagrama. Though no record of his work survives, writings of later Indian mathematicians suggest that he found a number of special cases of the Taylor series, including those for the trigonometric functions of sine, cosine, tangent, and arctangent. The Kerala School of Astronomy and Mathematics further expanded his works with various series expansions and rational approximations until the 16th century.

In the 17th century, James Gregory also worked in this area and published several Maclaurin series. It was not until 1715 however that a general method for constructing these series for all functions for which they exist was finally provided by Brook Taylor, after whom the series are now named.

The Maclaurin series was named after Colin Maclaurin, a professor in Edinburgh, who published the special case of the Taylor result in the 18th century.

Analytic functions

The function Template:Math is not analytic at Template:Math: the Taylor series is identically 0, although the function is not.

Template:Main If Template:Math is given by a convergent power series in an open disc (or interval in the real line) centred at Template:Mvar in the complex plane, it is said to be analytic in this disc. Thus for Template:Mvar in this disc, Template:Mvar is given by a convergent power series

$f(x)=\sum _{n=0}^{\infty }a_{n}(x-b)^{n}.$

Differentiating by Template:Mvar the above formula Template:Mvar times, then setting Template:Math gives:

${\frac {f^{(n)}(b)}{n!}}=a_{n}$

and so the power series expansion agrees with the Taylor series. Thus a function is analytic in an open disc centred at Template:Mvar if and only if its Taylor series converges to the value of the function at each point of the disc.

If Template:Math is equal to its Taylor series for all Template:Mvar in the complex plane, it is called entire. The polynomials, exponential function Template:Math, and the trigonometric functions sine and cosine, are examples of entire functions. Examples of functions that are not entire include the square root, the logarithm, the trigonometric function tangent, and its inverse, arctan. For these functions the Taylor series do not converge if Template:Mvar is far from Template:Mvar. That is, the Taylor series diverges at Template:Mvar if the distance between Template:Mvar and Template:Mvar is larger than the radius of convergence. The Taylor series can be used to calculate the value of an entire function at every point, if the value of the function, and of all of its derivatives, are known at a single point.

Uses of the Taylor series for analytic functions include:

1. The partial sums (the Taylor polynomials) of the series can be used as approximations of the function. These approximations are good if sufficiently many terms are included.
2. Differentiation and integration of power series can be performed term by term and is hence particularly easy.
3. An analytic function is uniquely extended to a holomorphic function on an open disk in the complex plane. This makes the machinery of complex analysis available.
4. The (truncated) series can be used to compute function values numerically, (often by recasting the polynomial into the Chebyshev form and evaluating it with the Clenshaw algorithm).
5. Algebraic operations can be done readily on the power series representation; for instance, Euler's formula follows from Taylor series expansions for trigonometric and exponential functions. This result is of fundamental importance in such fields as harmonic analysis.
6. Approximations using the first few terms of a Taylor series can make otherwise unsolvable problems possible for a restricted domain; this approach is often used in physics.

Approximation error and convergence

The sine function (blue) is closely approximated by its Taylor polynomial of degree 7 (pink) for a full period centered at the origin.

The Taylor polynomials for Template:Math only provide accurate approximations in the range Template:Math. For Template:Math, Taylor polynomials of higher degree provide worse approximations.

The Taylor approximations for Template:Math (black). For Template:Math, the approximations diverge.

Pictured on the right is an accurate approximation of Template:Math around the point Template:Math. The pink curve is a polynomial of degree seven:

$\sin \left(x\right)\approx x-{\frac {x^{3}}{3!}}+{\frac {x^{5}}{5!}}-{\frac {x^{7}}{7!}}.\!$

The error in this approximation is no more than Template:Math. In particular, for Template:Math, the error is less than 0.000003.

In contrast, also shown is a picture of the natural logarithm function Template:Math and some of its Taylor polynomials around Template:Math. These approximations converge to the function only in the region Template:Math; outside of this region the higher-degree Taylor polynomials are worse approximations for the function. This is similar to Runge's phenomenon.Template:Citation needed

The error incurred in approximating a function by its Template:Mvarth-degree Taylor polynomial is called the remainder or residual and is denoted by the function Template:Math. Taylor's theorem can be used to obtain a bound on the size of the remainder.

In general, Taylor series need not be convergent at all. And in fact the set of functions with a convergent Taylor series is a meager set in the Fréchet space of smooth functions. And even if the Taylor series of a function Template:Mvar does converge, its limit need not in general be equal to the value of the function Template:Math. For example, the function

$f(x)={\begin{cases}e^{-{\frac {1}{x^{2}}}}&{\text{if }}x\neq 0\\0&{\text{if }}x=0\end{cases}}$

is infinitely differentiable at Template:Math, and has all derivatives zero there. Consequently, the Taylor series of Template:Math about Template:Math is identically zero. However, Template:Math is not the zero function, so does not equal its Taylor series around the origin. Thus, Template:Math is an example of a non-analytic smooth function.

In real analysis, this example shows that there are infinitely differentiable functions Template:Math whose Taylor series are not equal to Template:Math even if they converge. By contrast, the holomorphic functions studied in complex analysis always possess a convergent Taylor series, and even the Taylor series of meromorphic functions, which might have singularities, never converge to a value different from the function itself. The complex function Template:Math, however, does not approach 0 when Template:Mvar approaches 0 along the imaginary axis, so it is not continuous in the complex plane and its Taylor series is undefined at 0.

More generally, every sequence of real or complex numbers can appear as coefficients in the Taylor series of an infinitely differentiable function defined on the real line, a consequence of Borel's lemma. As a result, the radius of convergence of a Taylor series can be zero. There are even infinitely differentiable functions defined on the real line whose Taylor series have a radius of convergence 0 everywhere.

A function cannot be written as a Taylor series centred at a singularity; in these cases, one can often still achieve a series expansion if one allows also negative powers of the variable Template:Mvar; see Laurent series. For example, Template:Math can be written as a Laurent series.

Generalization

There is, however, a generalization of the Taylor series that does converge to the value of the function itself for any bounded continuous function on Template:Math, using the calculus of finite differences. Specifically, one has the following theorem, due to Einar Hille, that for any Template:Math,

$\lim _{h\to 0^{+}}\sum _{n=0}^{\infty }{\frac {t^{n}}{n!}}{\frac {\Delta _{h}^{n}f(a)}{h^{n}}}=f(a+t).$

Here Template:Math is the Template:Mvarth finite difference operator with step size Template:Mvar. The series is precisely the Taylor series, except that divided differences appear in place of differentiation: the series is formally similar to the Newton series. When the function Template:Mvar is analytic at Template:Mvar, the terms in the series converge to the terms of the Taylor series, and in this sense generalizes the usual Taylor series.

In general, for any infinite sequence Template:Math, the following power series identity holds:

$\sum _{n=0}^{\infty }{\frac {u^{n}}{n!}}\Delta ^{n}a_{i}=e^{-u}\sum _{j=0}^{\infty }{\frac {u^{j}}{j!}}a_{i+j}.$

So in particular,

$f(a+t)=\lim _{h\to 0^{+}}e^{-{\frac {t}{h}}}\sum _{j=0}^{\infty }f(a+jh){\frac {\left({\frac {t}{h}}\right)^{j}}{j!}}.$

The series on the right is the expectation value of Template:Math, where Template:Mvar is a Poisson-distributed random variable that takes the value Template:Math with probability Template:Math. Hence,

$f(a+t)=\lim _{h\to 0^{+}}\int _{-\infty }^{\infty }f(a+x)dP_{{\frac {t}{h}},h}(x).$

The law of large numbers implies that the identity holds.

List of Maclaurin series of some common functions

Template:See also Several important Maclaurin series expansions follow. All these expansions are valid for complex arguments Template:Mvar.

Exponential function

The exponential function Template:Math (in blue), and the sum of the first Template:Math terms of its Taylor series at 0 (in red).

The exponential function $e^{x}$ (with base [[e (mathematics)|Template:Mvar]]) has Maclaurin series

$e^{x}=\sum _{n=0}^{\infty }{\frac {x^{n}}{n!}}=1+x+{\frac {x^{2}}{2!}}+{\frac {x^{3}}{3!}}+\cdots$.

It converges for all Template:Mvar.

Natural logarithm

The natural logarithm (with base [[e (mathematics)|Template:Mvar]]) has Maclaurin series

${\begin{aligned}\ln(1-x)&=-\sum _{n=1}^{\infty }{\frac {x^{n}}{n}}=-x-{\frac {x^{2}}{2}}-{\frac {x^{3}}{3}}-\cdots ,\\\ln(1+x)&=\sum _{n=1}^{\infty }(-1)^{n+1}{\frac {x^{n}}{n}}=x-{\frac {x^{2}}{2}}+{\frac {x^{3}}{3}}-\cdots .\end{aligned}}$

They converge for $|x|<1$. (In addition, the series for Template:Math converges for Template:Math, and the series for Template:Math converges for Template:Math.)

Geometric series

The geometric series and its derivatives have Maclaurin series

${\begin{aligned}{\frac {1}{1-x}}&=\sum _{n=0}^{\infty }x^{n}\\{\frac {1}{(1-x)^{2}}}&=\sum _{n=1}^{\infty }nx^{n-1}\\{\frac {1}{(1-x)^{3}}}&=\sum _{n=2}^{\infty }{\frac {(n-1)n}{2}}x^{n-2}.\end{aligned}}$

All are convergent for $|x|<1$. These are special cases of the binomial series given in the next section.

Binomial series

The binomial series is the power series

$(1+x)^{\alpha }=\sum _{n=0}^{\infty }{\binom {\alpha }{n}}x^{n}$

whose coefficients are the generalized binomial coefficients

${\binom {\alpha }{n}}=\prod _{k=1}^{n}{\frac {\alpha -k+1}{k}}={\frac {\alpha (\alpha -1)\cdots (\alpha -n+1)}{n!}}.$

(If Template:Math, this product is an empty product and has value 1.) It converges for $|x|<1$ for any real or complex number Template:Mvar.

When Template:Math, this is essentially the infinite geometric series mentioned in the previous section. The special cases Template:Math and Template:Math give the square root function and its inverse:

${\begin{aligned}(1+x)^{\frac {1}{2}}&=1+{\tfrac {1}{2}}x-{\tfrac {1}{8}}x^{2}+{\tfrac {1}{16}}x^{3}-{\tfrac {5}{128}}x^{4}+{\tfrac {7}{256}}x^{5}-\ldots ,\\(1+x)^{-{\frac {1}{2}}}&=1-{\tfrac {1}{2}}x+{\tfrac {3}{8}}x^{2}-{\tfrac {5}{16}}x^{3}+{\tfrac {35}{128}}x^{4}-{\tfrac {63}{256}}x^{5}+\ldots .\end{aligned}}$

When only the linear term is retained, this simplifies to the binomial approximation.

Trigonometric functions

The usual trigonometric functions and their inverses have the following Maclaurin series:

${\begin{aligned}\sin x&=\sum _{n=0}^{\infty }{\frac {(-1)^{n}}{(2n+1)!}}x^{2n+1}&&=x-{\frac {x^{3}}{3!}}+{\frac {x^{5}}{5!}}-\cdots &&{\text{for all }}x\\[6pt]\cos x&=\sum _{n=0}^{\infty }{\frac {(-1)^{n}}{(2n)!}}x^{2n}&&=1-{\frac {x^{2}}{2!}}+{\frac {x^{4}}{4!}}-\cdots &&{\text{for all }}x\\[6pt]\tan x&=\sum _{n=1}^{\infty }{\frac {B_{2n}(-4)^{n}\left(1-4^{n}\right)}{(2n)!}}x^{2n-1}&&=x+{\frac {x^{3}}{3}}+{\frac {2x^{5}}{15}}+\cdots &&{\text{for }}|x|<{\frac {\pi }{2}}\\[6pt]\sec x&=\sum _{n=0}^{\infty }{\frac {(-1)^{n}E_{2n}}{(2n)!}}x^{2n}&&=1+{\frac {x^{2}}{2}}+{\frac {5x^{4}}{24}}+\cdots &&{\text{for }}|x|<{\frac {\pi }{2}}\\[6pt]\arcsin x&=\sum _{n=0}^{\infty }{\frac {(2n)!}{4^{n}(n!)^{2}(2n+1)}}x^{2n+1}&&=x+{\frac {x^{3}}{6}}+{\frac {3x^{5}}{40}}+\cdots &&{\text{for }}|x|\leq 1\\[6pt]\arccos x&={\frac {\pi }{2}}-\arcsin x\\&={\frac {\pi }{2}}-\sum _{n=0}^{\infty }{\frac {(2n)!}{4^{n}(n!)^{2}(2n+1)}}x^{2n+1}&&={\frac {\pi }{2}}-x-{\frac {x^{3}}{6}}-{\frac {3x^{5}}{40}}-\cdots &&{\text{for }}|x|\leq 1\\[6pt]\arctan x&=\sum _{n=0}^{\infty }{\frac {(-1)^{n}}{2n+1}}x^{2n+1}&&=x-{\frac {x^{3}}{3}}+{\frac {x^{5}}{5}}-\cdots &&{\text{for }}|x|\leq 1,\ x\neq \pm i\end{aligned}}$

All angles are expressed in radians. The numbers Template:Math appearing in the expansions of Template:Math are the Bernoulli numbers. The Template:Math in the expansion of Template:Math are Euler numbers.

Hyperbolic functions

The hyperbolic functions have Maclaurin series closely related to the series for the corresponding trigonometric functions:

${\begin{aligned}\sinh x&=\sum _{n=0}^{\infty }{\frac {x^{2n+1}}{(2n+1)!}}&&=x+{\frac {x^{3}}{3!}}+{\frac {x^{5}}{5!}}+\cdots &&{\text{for all }}x\\[6pt]\cosh x&=\sum _{n=0}^{\infty }{\frac {x^{2n}}{(2n)!}}&&=1+{\frac {x^{2}}{2!}}+{\frac {x^{4}}{4!}}+\cdots &&{\text{for all }}x\\[6pt]\tanh x&=\sum _{n=1}^{\infty }{\frac {B_{2n}4^{n}\left(4^{n}-1\right)}{(2n)!}}x^{2n-1}&&=x-{\frac {x^{3}}{3}}+{\frac {2x^{5}}{15}}-{\frac {17x^{7}}{315}}+\cdots &&{\text{for }}|x|<{\frac {\pi }{2}}\\[6pt]\operatorname {arsinh} x&=\sum _{n=0}^{\infty }{\frac {(-1)^{n}(2n)!}{4^{n}(n!)^{2}(2n+1)}}x^{2n+1}&&&&{\text{for }}|x|\leq 1\\[6pt]\operatorname {artanh} x&=\sum _{n=0}^{\infty }{\frac {x^{2n+1}}{2n+1}}&&&&{\text{for }}|x|\leq 1,\ x\neq \pm 1\end{aligned}}$

The numbers Template:Math appearing in the series for Template:Math are the Bernoulli numbers.

Calculation of Taylor series

Several methods exist for the calculation of Taylor series of a large number of functions. One can attempt to use the definition of the Taylor series, though this often requires generalizing the form of the coefficients according to a readily apparent pattern. Alternatively, one can use manipulations such as substitution, multiplication or division, addition or subtraction of standard Taylor series to construct the Taylor series of a function, by virtue of Taylor series being power series. In some cases, one can also derive the Taylor series by repeatedly applying integration by parts. Particularly convenient is the use of computer algebra systems to calculate Taylor series.

First example

In order to compute the 7th degree Maclaurin polynomial for the function

$f(x)=\ln(\cos x),\quad x\in \left(-{\frac {\pi }{2}},{\frac {\pi }{2}}\right)$ ,

one may first rewrite the function as

$f(x)=\ln {\bigl (}1+(\cos x-1){\bigr )}\!$.

The Taylor series for the natural logarithm is (using the big O notation)

$\ln(1+x)=x-{\frac {x^{2}}{2}}+{\frac {x^{3}}{3}}+{O}\left(x^{4}\right)\!$

and for the cosine function

$\cos x-1=-{\frac {x^{2}}{2}}+{\frac {x^{4}}{24}}-{\frac {x^{6}}{720}}+{O}\left(x^{8}\right)\!$.

The latter series expansion has a zero constant term, which enables us to substitute the second series into the first one and to easily omit terms of higher order than the 7th degree by using the big Template:Mvar notation:

${\begin{aligned}f(x)&=\ln {\bigl (}1+(\cos x-1){\bigr )}\\&=(\cos x-1)-{\tfrac {1}{2}}(\cos x-1)^{2}+{\tfrac {1}{3}}(\cos x-1)^{3}+{O}\left((\cos x-1)^{4}\right)\\&=\left(-{\frac {x^{2}}{2}}+{\frac {x^{4}}{24}}-{\frac {x^{6}}{720}}+{O}\left(x^{8}\right)\right)-{\frac {1}{2}}\left(-{\frac {x^{2}}{2}}+{\frac {x^{4}}{24}}+{O}\left(x^{6}\right)\right)^{2}+{\frac {1}{3}}\left(-{\frac {x^{2}}{2}}+O\left(x^{4}\right)\right)^{3}+{O}\left(x^{8}\right)\\&=-{\frac {x^{2}}{2}}+{\frac {x^{4}}{24}}-{\frac {x^{6}}{720}}-{\frac {x^{4}}{8}}+{\frac {x^{6}}{48}}-{\frac {x^{6}}{24}}+O\left(x^{8}\right)\\&=-{\frac {x^{2}}{2}}-{\frac {x^{4}}{12}}-{\frac {x^{6}}{45}}+O\left(x^{8}\right).\end{aligned}}\!$

Since the cosine is an even function, the coefficients for all the odd powers Template:Math have to be zero.

Second example

Suppose we want the Taylor series at 0 of the function

$g(x)={\frac {e^{x}}{\cos x}}.\!$

We have for the exponential function

$e^{x}=\sum _{n=0}^{\infty }{\frac {x^{n}}{n!}}=1+x+{\frac {x^{2}}{2!}}+{\frac {x^{3}}{3!}}+{\frac {x^{4}}{4!}}+\cdots \!$

and, as in the first example,

$\cos x=1-{\frac {x^{2}}{2!}}+{\frac {x^{4}}{4!}}-\cdots \!$

Assume the power series is

${\frac {e^{x}}{\cos x}}=c_{0}+c_{1}x+c_{2}x^{2}+c_{3}x^{3}+\cdots \!$

Then multiplication with the denominator and substitution of the series of the cosine yields

${\begin{aligned}e^{x}&=\left(c_{0}+c_{1}x+c_{2}x^{2}+c_{3}x^{3}+\cdots \right)\cos x\\&=\left(c_{0}+c_{1}x+c_{2}x^{2}+c_{3}x^{3}+c_{4}x^{4}+\cdots \right)\left(1-{\frac {x^{2}}{2!}}+{\frac {x^{4}}{4!}}-\cdots \right)\\&=c_{0}-{\frac {c_{0}}{2}}x^{2}+{\frac {c_{0}}{4!}}x^{4}+c_{1}x-{\frac {c_{1}}{2}}x^{3}+{\frac {c_{1}}{4!}}x^{5}+c_{2}x^{2}-{\frac {c_{2}}{2}}x^{4}+{\frac {c_{2}}{4!}}x^{6}+c_{3}x^{3}-{\frac {c_{3}}{2}}x^{5}+{\frac {c_{3}}{4!}}x^{7}+c_{4}x^{4}+\cdots \end{aligned}}\!$

Collecting the terms up to fourth order yields

$e^{x}=c_{0}+c_{1}x+\left(c_{2}-{\frac {c_{0}}{2}}\right)x^{2}+\left(c_{3}-{\frac {c_{1}}{2}}\right)x^{3}+\left(c_{4}-{\frac {c_{2}}{2}}+{\frac {c_{0}}{4!}}\right)x^{4}+\cdots \!$

The values of $c_{i}$ can be found by comparison of coefficients with the top expression for $e^{x}$, yielding:

${\frac {e^{x}}{\cos x}}=1+x+x^{2}+{\frac {2x^{3}}{3}}+{\frac {x^{4}}{2}}+\cdots .\!$

Third example

Here we employ a method called "indirect expansion" to expand the given function. This method uses the known Taylor expansion of the exponential function. In order to expand Template:Math as a Taylor series in Template:Mvar, we use the known Taylor series of function Template:Math:

$e^{x}=\sum _{n=0}^{\infty }{\frac {x^{n}}{n!}}=1+x+{\frac {x^{2}}{2!}}+{\frac {x^{3}}{3!}}+{\frac {x^{4}}{4!}}+\cdots .$

Thus,

${\begin{aligned}(1+x)e^{x}&=e^{x}+xe^{x}=\sum _{n=0}^{\infty }{\frac {x^{n}}{n!}}+\sum _{n=0}^{\infty }{\frac {x^{n+1}}{n!}}=1+\sum _{n=1}^{\infty }{\frac {x^{n}}{n!}}+\sum _{n=0}^{\infty }{\frac {x^{n+1}}{n!}}\\&=1+\sum _{n=1}^{\infty }{\frac {x^{n}}{n!}}+\sum _{n=1}^{\infty }{\frac {x^{n}}{(n-1)!}}=1+\sum _{n=1}^{\infty }\left({\frac {1}{n!}}+{\frac {1}{(n-1)!}}\right)x^{n}\\&=1+\sum _{n=1}^{\infty }{\frac {n+1}{n!}}x^{n}\\&=\sum _{n=0}^{\infty }{\frac {n+1}{n!}}x^{n}.\end{aligned}}$

Taylor series as definitions

Classically, algebraic functions are defined by an algebraic equation, and transcendental functions (including those discussed above) are defined by some property that holds for them, such as a differential equation. For example, the exponential function is the function which is equal to its own derivative everywhere, and assumes the value 1 at the origin. However, one may equally well define an analytic function by its Taylor series.

Taylor series are used to define functions and "operators" in diverse areas of mathematics. In particular, this is true in areas where the classical definitions of functions break down. For example, using Taylor series, one may extend analytic functions to sets of matrices and operators, such as the matrix exponential or matrix logarithm.

In other areas, such as formal analysis, it is more convenient to work directly with the power series themselves. Thus one may define a solution of a differential equation as a power series which, one hopes to prove, is the Taylor series of the desired solution.

Taylor series in several variables

The Taylor series may also be generalized to functions of more than one variable with

${\begin{aligned}T(x_{1},\ldots ,x_{d})&=\sum _{n_{1}=0}^{\infty }\cdots \sum _{n_{d}=0}^{\infty }{\frac {(x_{1}-a_{1})^{n_{1}}\cdots (x_{d}-a_{d})^{n_{d}}}{n_{1}!\cdots n_{d}!}}\,\left({\frac {\partial ^{n_{1}+\cdots +n_{d}}f}{\partial x_{1}^{n_{1}}\cdots \partial x_{d}^{n_{d}}}}\right)(a_{1},\ldots ,a_{d})\\&=f(a_{1},\ldots ,a_{d})+\sum _{j=1}^{d}{\frac {\partial f(a_{1},\ldots ,a_{d})}{\partial x_{j}}}(x_{j}-a_{j})+{\frac {1}{2!}}\sum _{j=1}^{d}\sum _{k=1}^{d}{\frac {\partial ^{2}f(a_{1},\ldots ,a_{d})}{\partial x_{j}\partial x_{k}}}(x_{j}-a_{j})(x_{k}-a_{k})+\\&\qquad \qquad +{\frac {1}{3!}}\sum _{j=1}^{d}\sum _{k=1}^{d}\sum _{l=1}^{d}{\frac {\partial ^{3}f(a_{1},\ldots ,a_{d})}{\partial x_{j}\partial x_{k}\partial x_{l}}}(x_{j}-a_{j})(x_{k}-a_{k})(x_{l}-a_{l})+\cdots \end{aligned}}$

For example, for a function $f(x,y)$ that depends on two variables, Template:Mvar and Template:Mvar, the Taylor series to second order about the point Template:Math is

$f(a,b)+(x-a)f_{x}(a,b)+(y-b)f_{y}(a,b)+{\frac {1}{2!}}{\Big (}(x-a)^{2}f_{xx}(a,b)+2(x-a)(y-b)f_{xy}(a,b)+(y-b)^{2}f_{yy}(a,b){\Big )}$

where the subscripts denote the respective partial derivatives.

A second-order Taylor series expansion of a scalar-valued function of more than one variable can be written compactly as

$T(\mathbf {x} )=f(\mathbf {a} )+(\mathbf {x} -\mathbf {a} )^{\mathsf {T}}Df(\mathbf {a} )+{\frac {1}{2!}}(\mathbf {x} -\mathbf {a} )^{\mathsf {T}}\left\{D^{2}f(\mathbf {a} )\right\}(\mathbf {x} -\mathbf {a} )+\cdots ,$

where Template:Math is the gradient of Template:Mvar evaluated at Template:Math and Template:Math is the Hessian matrix. Applying the multi-index notation the Taylor series for several variables becomes

$T(\mathbf {x} )=\sum _{|\alpha |\geq 0}{\frac {(\mathbf {x} -\mathbf {a} )^{\alpha }}{\alpha !}}\left({\mathrm {\partial } ^{\alpha }}f\right)(\mathbf {a} ),$

which is to be understood as a still more abbreviated multi-index version of the first equation of this paragraph, with a full analogy to the single variable case.

Example

Second-order Taylor series approximation (in orange) of a function Template:Math around the origin.

In order to compute a second-order Taylor series expansion around point Template:Math of the function

$f(x,y)=e^{x}\ln(1+y),$

one first computes all the necessary partial derivatives:

${\begin{aligned}f_{x}&=e^{x}\ln(1+y)\\[6pt]f_{y}&={\frac {e^{x}}{1+y}}\\[6pt]f_{xx}&=e^{x}\ln(1+y)\\[6pt]f_{yy}&=-{\frac {e^{x}}{(1+y)^{2}}}\\[6pt]f_{xy}&=f_{yx}={\frac {e^{x}}{1+y}}.\end{aligned}}$

Evaluating these derivatives at the origin gives the Taylor coefficients

${\begin{aligned}f_{x}(0,0)&=0\\f_{y}(0,0)&=1\\f_{xx}(0,0)&=0\\f_{yy}(0,0)&=-1\\f_{xy}(0,0)&=f_{yx}(0,0)=1.\end{aligned}}$

Substituting these values in to the general formula

$T(x,y)=f(a,b)+(x-a)f_{x}(a,b)+(y-b)f_{y}(a,b)+{\frac {1}{2!}}{\Big (}(x-a)^{2}f_{xx}(a,b)+2(x-a)(y-b)f_{xy}(a,b)+(y-b)^{2}f_{yy}(a,b){\Big )}+\cdots$

produces

${\begin{aligned}T(x,y)&=0+0(x-0)+1(y-0)+{\frac {1}{2}}{\Big (}0(x-0)^{2}+2(x-0)(y-0)+(-1)(y-0)^{2}{\Big )}+\cdots \\&=y+xy-{\frac {y^{2}}{2}}+\cdots \end{aligned}}$

Since Template:Math is analytic in Template:Math, we have

$e^{x}\ln(1+y)=y+xy-{\frac {y^{2}}{2}}+\cdots ,\qquad |y|<1.$

Comparison with Fourier series

Template:Main The trigonometric Fourier series enables one to express a periodic function (or a function defined on a closed interval Template:Math) as an infinite sum of trigonometric functions (sines and cosines). In this sense, the Fourier series is analogous to Taylor series, since the latter allows one to express a function as an infinite sum of powers. Nevertheless, the two series differ from each other in several relevant issues:

• The finite truncations of the Taylor series of Template:Math about the point Template:Math are all exactly equal to Template:Math at Template:Math. In contrast, the Fourier series is computed by integrating over an entire interval, so there is generally no such point where all the finite truncations of the series are exact.
• The computation of Taylor series requires the knowledge of the function on an arbitrary small neighbourhood of a point, whereas the computation of the Fourier series requires knowing the function on its whole domain interval. In a certain sense one could say that the Taylor series is "local" and the Fourier series is "global".
• The Taylor series is defined for a function which has infinitely many derivatives at a single point, whereas the Fourier series is defined for any integrable function. In particular, the function could be nowhere differentiable. (For example, Template:Math could be a Weierstrass function.)
• The convergence of both series has very different properties. Even if the Taylor series has positive convergence radius, the resulting series may not coincide with the function; but if the function is analytic then the series converges pointwise to the function, and uniformly on every compact subset of the convergence interval. Concerning the Fourier series, if the function is square-integrable then the series converges in quadratic mean, but additional requirements are needed to ensure the pointwise or uniform convergence (for instance, if the function is periodic and of class C¹ then the convergence is uniform).
• Finally, in practice one wants to approximate the function with a finite number of terms, say with a Taylor polynomial or a partial sum of the trigonometric series, respectively. In the case of the Taylor series the error is very small in a neighbourhood of the point where it is computed, while it may be very large at a distant point. In the case of the Fourier series the error is distributed along the domain of the function.

See also

• Asymptotic expansion
• Generating function
• Laurent series
• Madhava series
• Newton's divided difference interpolation
• Padé approximant
• Puiseux series
• Shift operator

References

• Template:Citation
• Template:Citation
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External links

• Template:Springer
• Template:MathWorld
• Taylor polynomial - practical introduction
• Madhava of Sangamagramma
• "Discussion of the Parker-Sochacki Method"
• Another Taylor visualisation — where you can choose the point of the approximation and the number of derivatives
• Taylor series revisited for numerical methods at Numerical Methods for the STEM Undergraduate
• Cinderella 2: Taylor expansion
• Taylor series
• Inverse trigonometric functions Taylor series
• Template:Cite web