# Taylor's theorem

Taylor's theorem The exponential function y = ex (red) and the corresponding Taylor polynomial of degree four (dashed green) around the origin. Part of a series of articles about Calculus Fundamental theorem Leibniz integral rule Limits of functionsContinuity Mean value theoremRolle's theorem hide Differential Definitions Derivative (generalizations)Differential infinitesimalof a functiontotal Concepts Differentiation notationSecond derivativeImplicit differentiationLogarithmic differentiationRelated ratesTaylor's theorem Rules and identities SumProductChainPowerQuotientL'Hôpital's ruleInverseGeneral LeibnizFaà di Bruno's formulaReynolds show Integral show Series show Vector show Multivariable show Advanced show Specialized show Miscellaneous vte In calculus, Taylor's theorem gives an approximation of a k-times differentiable function around a given point by a polynomial of degree k, called the kth-order Taylor polynomial. For a smooth function, the Taylor polynomial is the truncation at the order k of the Taylor series of the function. The first-order Taylor polynomial is the linear approximation of the function, and the second-order Taylor polynomial is often referred to as the quadratic approximation.[1] There are several versions of Taylor's theorem, some giving explicit estimates of the approximation error of the function by its Taylor polynomial.

Taylor's theorem is named after the mathematician Brook Taylor, who stated a version of it in 1715,[2] although an earlier version of the result was already mentioned in 1671 by James Gregory.[3] Taylor's theorem is taught in introductory-level calculus courses and is one of the central elementary tools in mathematical analysis. It gives simple arithmetic formulas to accurately compute values of many transcendental functions such as the exponential function and trigonometric functions. It is the starting point of the study of analytic functions, and is fundamental in various areas of mathematics, as well as in numerical analysis and mathematical physics. Taylor's theorem also generalizes to multivariate and vector valued functions.

Contents 1 Motivation 2 Taylor's theorem in one real variable 2.1 Statement of the theorem 2.2 Explicit formulas for the remainder 2.3 Estimates for the remainder 2.4 Example 3 Relationship to analyticity 3.1 Taylor expansions of real analytic functions 3.2 Taylor's theorem and convergence of Taylor series 3.3 Taylor's theorem in complex analysis 3.4 Example 4 Generalizations of Taylor's theorem 4.1 Higher-order differentiability 4.2 Taylor's theorem for multivariate functions 4.3 Example in two dimensions 5 Proofs 5.1 Proof for Taylor's theorem in one real variable 5.2 Alternate proof for Taylor's theorem in one real variable 5.3 Derivation for the mean value forms of the remainder 5.4 Derivation for the integral form of the remainder 5.5 Derivation for the remainder of multivariate Taylor polynomials 6 See also 7 Footnotes 8 References 9 External links Motivation Graph of f(x) = ex (blue) with its linear approximation P1(x) = 1 + x (red) at a = 0.

If a real-valued function f(x) is differentiable at the point x = a, then it has a linear approximation near this point. This means that there exists a function h1(x) such that {displaystyle f(x)=f(a)+f'(a)(x-a)+h_{1}(x)(x-a),quad lim _{xto a}h_{1}(x)=0.} Here {displaystyle P_{1}(x)=f(a)+f'(a)(x-a)} is the linear approximation of f(x) for x near the point a, whose graph y = P1(x) is the tangent line to the graph y = f(x) at x = a. The error in the approximation is: {displaystyle R_{1}(x)=f(x)-P_{1}(x)=h_{1}(x)(x-a).} As x tends to a, this error goes to zero much faster than {displaystyle f'(a)(x{-}a)} , making {displaystyle f(x)approx P_{1}(x)} a useful approximation.

Graph of f(x) = ex (blue) with its quadratic approximation P2(x) = 1 + x + x2/2 (red) at a = 0. Note the improvement in the approximation.

For a better approximation to f(x), we can fit a quadratic polynomial instead of a linear function: {displaystyle P_{2}(x)=f(a)+f'(a)(x-a)+{frac {f''(a)}{2}}(x-a)^{2}.} Instead of just matching one derivative of f(x) at x = a, this polynomial has the same first and second derivatives, as is evident upon differentiation.

Taylor's theorem ensures that the quadratic approximation is, in a sufficiently small neighborhood of x = a, more accurate than the linear approximation. Specifically, {displaystyle f(x)=P_{2}(x)+h_{2}(x)(x-a)^{2},quad lim _{xto a}h_{2}(x)=0.} Here the error in the approximation is {displaystyle R_{2}(x)=f(x)-P_{2}(x)=h_{2}(x)(x-a)^{2},} which, given the limiting behavior of {displaystyle h_{2}} , goes to zero faster than {displaystyle (x-a)^{2}} as x tends to a.

Approximation of f(x) = 1/(1 + x2) (blue) by its Taylor polynomials Pk of order k = 1, …, 16 centered at x = 0 (red) and x = 1 (green). The approximations do not improve at all outside (−1, 1) and (1 − √2, 1 + √2) respectively.

Similarly, we might get still better approximations to f if we use polynomials of higher degree, since then we can match even more derivatives with f at the selected base point.

In general, the error in approximating a function by a polynomial of degree k will go to zero much faster than {displaystyle (x-a)^{k}} as x tends to a. However, there are functions, even infinitely differentiable ones, for which increasing the degree of the approximating polynomial does not increase the accuracy of approximation: we say such a function fails to be analytic at x = a: it is not (locally) determined by its derivatives at this point.

Taylor's theorem is of asymptotic nature: it only tells us that the error Rk in an approximation by a k-th order Taylor polynomial Pk tends to zero faster than any nonzero k-th degree polynomial as x → a. It does not tell us how large the error is in any concrete neighborhood of the center of expansion, but for this purpose there are explicit formulas for the remainder term (given below) which are valid under some additional regularity assumptions on f. These enhanced versions of Taylor's theorem typically lead to uniform estimates for the approximation error in a small neighborhood of the center of expansion, but the estimates do not necessarily hold for neighborhoods which are too large, even if the function f is analytic. In that situation one may have to select several Taylor polynomials with different centers of expansion to have reliable Taylor-approximations of the original function (see animation on the right.) There are several ways we might use the remainder term: Estimate the error for a polynomial Pk(x) of degree k estimating f(x) on a given interval (a – r, a + r). (Given the interval and degree, we find the error.) Find the smallest degree k for which the polynomial Pk(x) approximates f(x) to within a given error tolerance on a given interval (a − r, a + r) . (Given the interval and error tolerance, we find the degree.) Find the largest interval (a − r, a + r) on which Pk(x) approximates f(x) to within a given error tolerance. (Given the degree and error tolerance, we find the interval.) Taylor's theorem in one real variable Statement of the theorem The precise statement of the most basic version of Taylor's theorem is as follows: Taylor's theorem[4][5][6] — Let k ≥ 1 be an integer and let the function f : R → R be k times differentiable at the point a ∈ R. Then there exists a function hk : R → R such that {displaystyle f(x)=f(a)+f'(a)(x-a)+{frac {f''(a)}{2!}}(x-a)^{2}+cdots +{frac {f^{(k)}(a)}{k!}}(x-a)^{k}+h_{k}(x)(x-a)^{k},} and {displaystyle lim _{xto a}h_{k}(x)=0.} This is called the Peano form of the remainder.

The polynomial appearing in Taylor's theorem is the k-th order Taylor polynomial {displaystyle P_{k}(x)=f(a)+f'(a)(x-a)+{frac {f''(a)}{2!}}(x-a)^{2}+cdots +{frac {f^{(k)}(a)}{k!}}(x-a)^{k}} of the function f at the point a. The Taylor polynomial is the unique "asymptotic best fit" polynomial in the sense that if there exists a function hk : R → R and a k-th order polynomial p such that {displaystyle f(x)=p(x)+h_{k}(x)(x-a)^{k},quad lim _{xto a}h_{k}(x)=0,} then p = Pk. Taylor's theorem describes the asymptotic behavior of the remainder term {displaystyle R_{k}(x)=f(x)-P_{k}(x),} which is the approximation error when approximating f with its Taylor polynomial. Using the little-o notation, the statement in Taylor's theorem reads as {displaystyle R_{k}(x)=o(|x-a|^{k}),quad xto a.} Explicit formulas for the remainder Under stronger regularity assumptions on f there are several precise formulas for the remainder term Rk of the Taylor polynomial, the most common ones being the following.

Mean-value forms of the remainder — Let f : R → R be k + 1 times differentiable on the open interval with f(k) continuous on the closed interval between a and x.[7] Then {displaystyle R_{k}(x)={frac {f^{(k+1)}(xi _{L})}{(k+1)!}}(x-a)^{k+1}} for some real number ξL between a and x. This is the Lagrange form[8] of the remainder.

Similarly, {displaystyle R_{k}(x)={frac {f^{(k+1)}(xi _{C})}{k!}}(x-xi _{C})^{k}(x-a)} for some real number ξC between a and x. This is the Cauchy form[9] of the remainder.

These refinements of Taylor's theorem are usually proved using the mean value theorem, whence the name. Additionally, notice that this is precisely the mean value theorem when k = 0. Also other similar expressions can be found. For example, if G(t) is continuous on the closed interval and differentiable with a non-vanishing derivative on the open interval between a and x, then {displaystyle R_{k}(x)={frac {f^{(k+1)}(xi )}{k!}}(x-xi )^{k}{frac {G(x)-G(a)}{G'(xi )}}} for some number ξ between a and x. This version covers the Lagrange and Cauchy forms of the remainder as special cases, and is proved below using Cauchy's mean value theorem.

The statement for the integral form of the remainder is more advanced than the previous ones, and requires understanding of Lebesgue integration theory for the full generality. However, it holds also in the sense of Riemann integral provided the (k + 1)th derivative of f is continuous on the closed interval [a,x].

Integral form of the remainder[10] — Let f(k) be absolutely continuous on the closed interval between a and x. Then {displaystyle R_{k}(x)=int _{a}^{x}{frac {f^{(k+1)}(t)}{k!}}(x-t)^{k},dt.} Due to absolute continuity of f(k) on the closed interval between a and x, its derivative f(k+1) exists as an L1-function, and the result can be proven by a formal calculation using fundamental theorem of calculus and integration by parts.

Estimates for the remainder It is often useful in practice to be able to estimate the remainder term appearing in the Taylor approximation, rather than having an exact formula for it. Suppose that f is (k + 1)-times continuously differentiable in an interval I containing a. Suppose that there are real constants q and Q such that {displaystyle qleq f^{(k+1)}(x)leq Q} throughout I. Then the remainder term satisfies the inequality[11] {displaystyle q{frac {(x-a)^{k+1}}{(k+1)!}}leq R_{k}(x)leq Q{frac {(x-a)^{k+1}}{(k+1)!}},} if x > a, and a similar estimate if x < a. This is a simple consequence of the Lagrange form of the remainder. In particular, if {displaystyle |f^{(k+1)}(x)|leq M} on an interval I = (a − r,a + r) with some {displaystyle r>0} , then {displaystyle |R_{k}(x)|leq M{frac {|x-a|^{k+1}}{(k+1)!}}leq M{frac {r^{k+1}}{(k+1)!}}} for all x∈(a − r,a + r). The second inequality is called a uniform estimate, because it holds uniformly for all x on the interval (a − r,a + r).

Example Approximation of ex (blue) by its Taylor polynomials Pk of order k = 1,…,7 centered at x = 0 (red).

Suppose that we wish to find the approximate value of the function f(x) = ex on the interval [−1,1] while ensuring that the error in the approximation is no more than 10−5. In this example we pretend that we only know the following properties of the exponential function: {displaystyle e^{0}=1,qquad {frac {d}{dx}}e^{x}=e^{x},qquad e^{x}>0,qquad xin mathbb {R} .} (⁎) From these properties it follows that f(k)(x) = ex for all k, and in particular, f(k)(0) = 1. Hence the k-th order Taylor polynomial of f at 0 and its remainder term in the Lagrange form are given by {displaystyle P_{k}(x)=1+x+{frac {x^{2}}{2!}}+cdots +{frac {x^{k}}{k!}},qquad R_{k}(x)={frac {e^{xi }}{(k+1)!}}x^{k+1},} where ξ is some number between 0 and x. Since ex is increasing by (⁎), we can simply use ex ≤ 1 for x ∈ [−1, 0] to estimate the remainder on the subinterval [−1, 0]. To obtain an upper bound for the remainder on [0,1], we use the property eξ < ex for 0<ξ

However, as k increases for fixed r, the value of Mk,r grows more quickly than rk, and the error does not go to zero.

Taylor's theorem in complex analysis Taylor's theorem generalizes to functions f : C → C which are complex differentiable in an open subset U ⊂ C of the complex plane. However, its usefulness is dwarfed by other general theorems in complex analysis. Namely, stronger versions of related results can be deduced for complex differentiable functions f : U → C using Cauchy's integral formula as follows.

Let r > 0 such that the closed disk B(z, r) ∪ S(z, r) is contained in U. Then Cauchy's integral formula with a positive parametrization γ(t) = z + reit of the circle S(z, r) with t ∈ [0, 2π] gives {displaystyle f(z)={frac {1}{2pi i}}int _{gamma }{frac {f(w)}{w-z}},dw,quad f'(z)={frac {1}{2pi i}}int _{gamma }{frac {f(w)}{(w-z)^{2}}},dw,quad ldots ,quad f^{(k)}(z)={frac {k!}{2pi i}}int _{gamma }{frac {f(w)}{(w-z)^{k+1}}},dw.} Here all the integrands are continuous on the circle S(z, r), which justifies differentiation under the integral sign. In particular, if f is once complex differentiable on the open set U, then it is actually infinitely many times complex differentiable on U. One also obtains the Cauchy's estimates[12] {displaystyle |f^{(k)}(z)|leq {frac {k!}{2pi }}int _{gamma }{frac {M_{r}}{|w-z|^{k+1}}},dw={frac {k!M_{r}}{r^{k}}},quad M_{r}=max _{|w-c|=r}|f(w)|} for any z ∈ U and r > 0 such that B(z, r) ∪ S(c, r) ⊂ U. These estimates imply that the complex Taylor series {displaystyle T_{f}(z)=sum _{k=0}^{infty }{frac {f^{(k)}(c)}{k!}}(z-c)^{k}} of f converges uniformly on any open disk B(c, r) ⊂ U with S(c, r) ⊂ U into some function Tf. Furthermore, using the contour integral formulas for the derivatives f(k)(c), {displaystyle {begin{aligned}T_{f}(z)&=sum _{k=0}^{infty }{frac {(z-c)^{k}}{2pi i}}int _{gamma }{frac {f(w)}{(w-c)^{k+1}}},dw\&={frac {1}{2pi i}}int _{gamma }{frac {f(w)}{w-c}}sum _{k=0}^{infty }left({frac {z-c}{w-c}}right)^{k},dw\&={frac {1}{2pi i}}int _{gamma }{frac {f(w)}{w-c}}left({frac {1}{1-{frac {z-c}{w-c}}}}right),dw\&={frac {1}{2pi i}}int _{gamma }{frac {f(w)}{w-z}},dw=f(z),end{aligned}}} so any complex differentiable function f in an open set U ⊂ C is in fact complex analytic. All that is said for real analytic functions here holds also for complex analytic functions with the open interval I replaced by an open subset U ∈ C and a-centered intervals (a − r, a + r) replaced by c-centered disks B(c, r). In particular, the Taylor expansion holds in the form {displaystyle f(z)=P_{k}(z)+R_{k}(z),quad P_{k}(z)=sum _{j=0}^{k}{frac {f^{(j)}(c)}{j!}}(z-c)^{j},} where the remainder term Rk is complex analytic. Methods of complex analysis provide some powerful results regarding Taylor expansions. For example, using Cauchy's integral formula for any positively oriented Jordan curve γ which parametrizes the boundary ∂W ⊂ U of a region W ⊂ U, one obtains expressions for the derivatives f(j)(c) as above, and modifying slightly the computation for Tf(z) = f(z), one arrives at the exact formula {displaystyle R_{k}(z)=sum _{j=k+1}^{infty }{frac {(z-c)^{j}}{2pi i}}int _{gamma }{frac {f(w)}{(w-c)^{j+1}}},dw={frac {(z-c)^{k+1}}{2pi i}}int _{gamma }{frac {f(w),dw}{(w-c)^{k+1}(w-z)}},qquad zin W.} The important feature here is that the quality of the approximation by a Taylor polynomial on the region W ⊂ U is dominated by the values of the function f itself on the boundary ∂W ⊂ U. Similarly, applying Cauchy's estimates to the series expression for the remainder, one obtains the uniform estimates {displaystyle |R_{k}(z)|leq sum _{j=k+1}^{infty }{frac {M_{r}|z-c|^{j}}{r^{j}}}={frac {M_{r}}{r^{k+1}}}{frac {|z-c|^{k+1}}{1-{frac {|z-c|}{r}}}}leq {frac {M_{r}beta ^{k+1}}{1-beta }},qquad {frac {|z-c|}{r}}leq beta <1.} Example Complex plot of f(z) = 1/(1 + z2). Modulus is shown by elevation and argument by coloring: cyan=0, blue = π/3, violet = 2π/3, red = π, yellow=4π/3, green=5π/3. The function {displaystyle {begin{aligned}&f:mathbb {R} to mathbb {R} \&f(x)={frac {1}{1+x^{2}}}end{aligned}}} is real analytic, that is, locally determined by its Taylor series. This function was plotted above to illustrate the fact that some elementary functions cannot be approximated by Taylor polynomials in neighborhoods of the center of expansion which are too large. This kind of behavior is easily understood in the framework of complex analysis. Namely, the function f extends into a meromorphic function {displaystyle {begin{aligned}&f:mathbb {C} cup {infty }to mathbb {C} cup {infty }\&f(z)={frac {1}{1+z^{2}}}end{aligned}}} on the compactified complex plane. It has simple poles at z = i and z = −i, and it is analytic elsewhere. Now its Taylor series centered at z0 converges on any disc B(z0, r) with r < |z − z0|, where the same Taylor series converges at z ∈ C. Therefore, Taylor series of f centered at 0 converges on B(0, 1) and it does not converge for any z ∈ C with |z| > 1 due to the poles at i and −i. For the same reason the Taylor series of f centered at 1 converges on B(1, √2) and does not converge for any z ∈ C with |z − 1| > √2.

Generalizations of Taylor's theorem Higher-order differentiability A function f: Rn → R is differentiable at a ∈ Rn if and only if there exists a linear functional L : Rn → R and a function h : Rn → R such that {displaystyle f({boldsymbol {x}})=f({boldsymbol {a}})+L({boldsymbol {x}}-{boldsymbol {a}})+h({boldsymbol {x}})lVert {boldsymbol {x}}-{boldsymbol {a}}rVert ,qquad lim _{{boldsymbol {x}}to {boldsymbol {a}}}h({boldsymbol {x}})=0.} If this is the case, then L = df(a) is the (uniquely defined) differential of f at the point a. Furthermore, then the partial derivatives of f exist at a and the differential of f at a is given by {displaystyle df({boldsymbol {a}})({boldsymbol {v}})={frac {partial f}{partial x_{1}}}({boldsymbol {a}})v_{1}+cdots +{frac {partial f}{partial x_{n}}}({boldsymbol {a}})v_{n}.} Introduce the multi-index notation {displaystyle |alpha |=alpha _{1}+cdots +alpha _{n},quad alpha !=alpha _{1}!cdots alpha _{n}!,quad {boldsymbol {x}}^{alpha }=x_{1}^{alpha _{1}}cdots x_{n}^{alpha _{n}}} for α ∈ Nn and x ∈ Rn. If all the k-th order partial derivatives of f : Rn → R are continuous at a ∈ Rn, then by Clairaut's theorem, one can change the order of mixed derivatives at a, so the notation {displaystyle D^{alpha }f={frac {partial ^{|alpha |}f}{partial x_{1}^{alpha _{1}}cdots partial x_{n}^{alpha _{n}}}},qquad |alpha |leq k} for the higher order partial derivatives is justified in this situation. The same is true if all the (k − 1)-th order partial derivatives of f exist in some neighborhood of a and are differentiable at a.[13] Then we say that f is k times differentiable at the point a.

Taylor's theorem for multivariate functions Using notations of the preceding section, one has the following theorem.

Multivariate version of Taylor's theorem[14] — Let f : Rn → R be a k-times continuously differentiable function at the point a ∈ Rn. Then there exist functions hα : Rn → R, where {displaystyle |alpha |=k,} such that {displaystyle {begin{aligned}&f({boldsymbol {x}})=sum _{|alpha |leq k}{frac {D^{alpha }f({boldsymbol {a}})}{alpha !}}({boldsymbol {x}}-{boldsymbol {a}})^{alpha }+sum _{|alpha |=k+1}h_{alpha }({boldsymbol {x}})({boldsymbol {x}}-{boldsymbol {a}})^{alpha },\&{mbox{and}}quad lim _{{boldsymbol {x}}to {boldsymbol {a}}}h_{alpha }({boldsymbol {x}})=0.end{aligned}}} If the function f : Rn → R is k + 1 times continuously differentiable in a closed ball {displaystyle B={mathbf {y} in mathbb {R} ^{n}:left|mathbf {a} -mathbf {y} right|leq r}} for some {displaystyle r>0} , then one can derive an exact formula for the remainder in terms of (k+1)-th order partial derivatives of f in this neighborhood.[15] Namely, {displaystyle {begin{aligned}&f({boldsymbol {x}})=sum _{|alpha |leq k}{frac {D^{alpha }f({boldsymbol {a}})}{alpha !}}({boldsymbol {x}}-{boldsymbol {a}})^{alpha }+sum _{|beta |=k+1}R_{beta }({boldsymbol {x}})({boldsymbol {x}}-{boldsymbol {a}})^{beta },\&R_{beta }({boldsymbol {x}})={frac {|beta |}{beta !}}int _{0}^{1}(1-t)^{|beta |-1}D^{beta }f{big (}{boldsymbol {a}}+t({boldsymbol {x}}-{boldsymbol {a}}){big )},dt.end{aligned}}} In this case, due to the continuity of (k+1)-th order partial derivatives in the compact set B, one immediately obtains the uniform estimates {displaystyle left|R_{beta }({boldsymbol {x}})right|leq {frac {1}{beta !}}max _{|alpha |=|beta |}max _{{boldsymbol {y}}in B}|D^{alpha }f({boldsymbol {y}})|,qquad {boldsymbol {x}}in B.} Example in two dimensions For example, the third-order Taylor polynomial of a smooth function f: R2 → R is, denoting x − a = v, {displaystyle {begin{aligned}P_{3}({boldsymbol {x}})=f({boldsymbol {a}})+{}&{frac {partial f}{partial x_{1}}}({boldsymbol {a}})v_{1}+{frac {partial f}{partial x_{2}}}({boldsymbol {a}})v_{2}+{frac {partial ^{2}f}{partial x_{1}^{2}}}({boldsymbol {a}}){frac {v_{1}^{2}}{2!}}+{frac {partial ^{2}f}{partial x_{1}partial x_{2}}}({boldsymbol {a}})v_{1}v_{2}+{frac {partial ^{2}f}{partial x_{2}^{2}}}({boldsymbol {a}}){frac {v_{2}^{2}}{2!}}\&+{frac {partial ^{3}f}{partial x_{1}^{3}}}({boldsymbol {a}}){frac {v_{1}^{3}}{3!}}+{frac {partial ^{3}f}{partial x_{1}^{2}partial x_{2}}}({boldsymbol {a}}){frac {v_{1}^{2}v_{2}}{2!}}+{frac {partial ^{3}f}{partial x_{1}partial x_{2}^{2}}}({boldsymbol {a}}){frac {v_{1}v_{2}^{2}}{2!}}+{frac {partial ^{3}f}{partial x_{2}^{3}}}({boldsymbol {a}}){frac {v_{2}^{3}}{3!}}end{aligned}}} Proofs Proof for Taylor's theorem in one real variable Let[16] {displaystyle h_{k}(x)={begin{cases}{frac {f(x)-P(x)}{(x-a)^{k}}}&xnot =a\0&x=aend{cases}}} where, as in the statement of Taylor's theorem, {displaystyle P(x)=f(a)+f'(a)(x-a)+{frac {f''(a)}{2!}}(x-a)^{2}+cdots +{frac {f^{(k)}(a)}{k!}}(x-a)^{k}.} It is sufficient to show that {displaystyle lim _{xto a}h_{k}(x)=0.} The proof here is based on repeated application of L'Hôpital's rule. Note that, for each j = 0,1,…,k−1, {displaystyle f^{(j)}(a)=P^{(j)}(a)} . Hence each of the first k−1 derivatives of the numerator in {displaystyle h_{k}(x)} vanishes at {displaystyle x=a} , and the same is true of the denominator. Also, since the condition that the function f be k times differentiable at a point requires differentiability up to order k−1 in a neighborhood of said point (this is true, because differentiability requires a function to be defined in a whole neighborhood of a point), the numerator and its k − 2 derivatives are differentiable in a neighborhood of a. Clearly, the denominator also satisfies said condition, and additionally, doesn't vanish unless x=a, therefore all conditions necessary for L'Hopital's rule are fulfilled, and its use is justified. So {displaystyle {begin{aligned}lim _{xto a}{frac {f(x)-P(x)}{(x-a)^{k}}}&=lim _{xto a}{frac {{frac {d}{dx}}(f(x)-P(x))}{{frac {d}{dx}}(x-a)^{k}}}=cdots =lim _{xto a}{frac {{frac {d^{k-1}}{dx^{k-1}}}(f(x)-P(x))}{{frac {d^{k-1}}{dx^{k-1}}}(x-a)^{k}}}\&={frac {1}{k!}}lim _{xto a}{frac {f^{(k-1)}(x)-P^{(k-1)}(x)}{x-a}}\&={frac {1}{k!}}(f^{(k)}(a)-f^{(k)}(a))=0end{aligned}}} where the last equality follows by the definition of the derivative at x = a.

Alternate proof for Taylor's theorem in one real variable Let {displaystyle f(x)} be any real-valued, continuous, function to be approximated by the Taylor polynomial.

Step 1: Let F and G be functions. Set F and G to be {displaystyle {begin{aligned}F(x)=f(x)-sum _{k=0}^{n-1}{frac {f^{(k)}(a)}{k!}}(x-a)^{k}end{aligned}}} {displaystyle {begin{aligned}G(x)=(x-a)^{n}end{aligned}}} Step 2: Properties of F and G: {displaystyle {begin{aligned}F(a)=f(a)-f(a)-f'(a)(a-a)-...-{frac {f^{(n-1)}(a)(a-a)^{n-1}}{(n-1)!}}end{aligned}}} Similarly, {displaystyle {begin{aligned}F'(a)=f'(a)-f'(a)-{frac {2f''(a)(a-a)}{1!}}-...-{frac {f^{(n-2)}(a)(n-1)(a-a)^{n-2}}{(n-1)!}}=0end{aligned}}} {displaystyle {begin{aligned}G'(a)=n(a-a)^{n-1}=0end{aligned}}} . . .

{displaystyle {begin{aligned}G^{(n-1)}(a)=F^{(n-1)}(a)=0end{aligned}}} Step 3: Use Cauchy Mean Value Theorem Let {displaystyle f_{1}} and {displaystyle g_{1}} be continuous functions on {displaystyle [a,b]} . Since {displaystyle a

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