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Let \(E\) be an open set in \(R^{n}\). The classes \(\mathscr{E}^{\prime}(E)\) and \(\mathscr{}^{\circ}(E)\) are defined in the text. By induction, \(\mathscr{G}^{(\omega)}(E)\) can be defined as follows, for all positive integers \(k:\) To say that \(f \in \mathscr{G}^{(1)}(E)\) means that the partial derivatives \(D_{1} f, \ldots, D_{n} f\) belong to \(8^{(k-1)}(E)\). Assume \(f \in \mathscr{G}^{(n)}(E)\), and show (by repeated application of Theorem 9.41) that the \(k\) th-order derivative $$ D_{i_{1} i_{2} \ldots i_{k}} f=D_{i_{1}} D_{i_{2}} \ldots D_{i_{k}} f $$ is unchanged if the subscripts \(i_{1}, \ldots, i_{k}\) are permuted. For instance, if \(n \geq 3\), then $$ D_{1213} f=D_{3112} f $$ for every \(f \in \mathscr{Y}^{(4)}\).

Short Answer

Expert verified
Question: Prove that the kth-order derivative of a function \(f\in\mathscr{G}^{(n)}(E)\) remains unchanged under any permutation of the subscripts \(i_1, \ldots, i_k\). Answer: Using induction, we showed that the kth-order derivative of a function \(f\in\mathscr{G}^{(n)}(E)\) is the same under any permutation of the subscripts \(i_1, \ldots, i_k\), which is given by \(D_{i_1 i_2 \ldots i_k} f = D_{\sigma(i_1) \sigma(i_2) \ldots \sigma(i_k)} f\).

Step by step solution

01

Base Case k=1 (First-Order Derivatives)

For the case when \(k = 1\), there is only one subscript, \(i_1\). This means that there is no permutation possible, and the statement is true by default.
02

Inductive Hypothesis

Assume that the statement is true for some \(k \geq 1\). That is: $$ D_{i_1 i_2 \ldots i_k} f = D_{\sigma(i_1) \sigma(i_2) \ldots \sigma(i_k)} f $$ for any permutation \(\sigma\) of the subscripts \(i_1, \ldots, i_k\), where \(f \in \mathscr{G}^{(n)}(E)\).
03

Induction Step (k+1) (Using Theorem 9.41)

We want to show that the statement is also true for \(k+1\). That is, we want to show that: $$ D_{i_1 i_2 \ldots i_{k+1}} f = D_{\sigma(i_1) \sigma(i_2) \ldots \sigma(i_{k+1})} f $$ for any permutation \(\sigma\) of the subscripts \(i_1, \ldots, i_{k+1}\). From our inductive hypothesis, we know that: $$ D_{i_1 i_2 \ldots i_k} f = D_{\sigma(i_1) \sigma(i_2) \ldots \sigma(i_k)} f $$ Since \(f \in \mathscr{G}^{(n)}(E)\), we know that all its \(k\)th-order partial derivatives belong to \(\mathscr{G}^{(n-1)}(E)\). Therefore, by Theorem 9.41, we have: $$ D_{i_{k+1}} (D_{i_1 i_2 \ldots i_k} f) = D_{i_{k+1}} (D_{\sigma(i_1) \sigma(i_2) \ldots \sigma(i_k)} f) $$ Using the chain rule for higher-order derivatives, we can rewrite the left-hand side of the equation as: $$ D_{i_1 i_2 \ldots i_{k+1}} f = D_{i_{k+1}} (D_{i_1 i_2 \ldots i_k} f) $$ Similarly, we can rewrite the right-hand side of the equation as: $$ D_{\sigma(i_1) \sigma(i_2) \ldots \sigma(i_{k+1})} f = D_{i_{k+1}} (D_{\sigma(i_1) \sigma(i_2) \ldots \sigma(i_k)} f) $$
04

Conclusion

Thus, we have shown that: $$ D_{i_1 i_2 \ldots i_{k+1}} f = D_{\sigma(i_1) \sigma(i_2) \ldots \sigma(i_{k+1})} f $$ for any permutation \(\sigma\) of the subscripts \(i_1, \ldots, i_{k+1}\), assuming the statement is true for \(k\). Therefore, by induction, the statement is true for all \(k \geq 1\) and the proof is complete.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Partial Derivatives
In multivariable calculus, partial derivatives play a crucial role in analyzing the behavior of functions with several independent variables. When we have a function, say, of two variables, such as temperature over a geographical area where it depends on both the latitude and the longitude, a partial derivative represents how the temperature changes with respect to one variable while keeping the other constant.

For instance, the notation \( \frac{\partial f}{\partial x} \) denotes a partial derivative of the function \( f(x, y) \) with respect to \( x \), while \( y \) is held fixed. This concept is extended to functions of three or more variables similarly. Over an open set in \( \mathbb{R}^n \), the existence and continuity of all relevant partial derivatives are significant for various theorems in calculus, such as the differentiability of functions.
Higher-Order Derivatives
As we delve further into the study of calculus, we encounter higher-order derivatives, which are essentially the 'derivatives of derivatives'. When the first derivative represents the rate of change, the second derivative provides information about the curvature or acceleration of a function, and so on. The notation \( D_{i_1 i_2 \ldots i_k} f \) represents a k-th order derivative of function \( f \) with respect to variables \( i_1, i_2, \ldots, i_k \).

Significance in Mathematics and Physics

Higher-order derivatives are fundamental in fields like physics, where the third derivative of position with respect to time is known as jerk, and higher derivatives are often examined in the study of motion. In a mathematical context, understanding the behavior of higher-order derivatives helps in Taylor series expansions, optimization problems, and differential equations, among others.
Inductive Proof
Inductive proofs are a paradigm of mathematical reasoning that allows us to prove statements for all natural numbers. It's a two-step process involving a base case and an inductive step. The base case verifies the statement for the initial value, whereas the inductive step proves that if the statement holds for an arbitrary natural number \( k \), it must also hold for \( k+1 \).

This logical process ensures that the property is true for all subsequent numbers, effectively proving the statement infinitely. Inductive proofs are extensively used in theorems concerning series, sequences, and, as seen in the original exercise, the symmetry of higher-order derivatives in multivariable calculus.
Multivariable Calculus
Multivariable calculus extends the concepts of single-variable calculus to higher dimensions, addressing functions of several variables. Fundamental notions encompass vector-valued functions, partial derivatives, multiple integrals, line and surface integrals, and even vector fields.

Applications

Its applications are vast, influencing areas such as physics for understanding electromagnetic fields or fluid dynamics, economics for multivariate optimization scenarios, and even in computer graphics for modeling surfaces and transformations. The exercise presented dives into a component of this larger field, focusing on the invariance of the order of partial differentiation, which is central to the robustness and predictability of mathematical models involving multiple variables.

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Most popular questions from this chapter

If \(f(0,0)=0\) and $$ f(x, y)=\frac{x y}{x^{2}+y^{2}} \quad \text { if }(x, y) \neq(0,0) $$ prove that \(\left(D_{1} f\right)(x, y)\) and \(\left(D_{2} f\right)(x, y)\) exist at every point of \(R^{2}\), although \(f\) is not continuous at \((0.0)\).

Define \(f(0,0)=0\) and $$ f(x, y)=\frac{x^{3}}{x^{2}+y^{2}} \quad \text { if }(x, y) \neq(0,0) $$ (a) Prove that \(D_{1} f\) and \(D_{2} f\) are bounded functions in \(R^{2}\). (Hence \(f\) is continuous.) (b) Let u be any unit vector in \(R^{2}\). Show that the directional derivative \(\left(D_{w} f\right)(0,0)\) exists, and that its absolute value is at most 1 . (c) Let \(\gamma\) be a differentiable mapping of \(R^{1}\) into \(R^{2}\) (in other words, \(\gamma\) is a differentiable curve in \(\left.R^{2}\right)\), with \(\gamma(0)=(0,0)\) and \(\left|\gamma^{\prime}(0)\right|>0 .\) Put \(g(t)=f(\gamma(t))\) and prove that \(g\) is differentiable for every \(t \in R^{1}\). If \(\gamma \in \mathscr{C}^{\prime}\), prove that \(g \in \mathscr{8}\) '. (d) In spite of this, prove that \(f\) is not differentiable at \((0,0)\). Hint: Formula (40) fails.

Prove (as asserted in Sec. 9.30) that null spaces and ranges of linear transformations are vector spaces.

If \(S\) is a nonempty subset of a vector space \(X\), prove (as asserted in Sec. 9.1) that the span of \(S\) is a vector space.

Let \(f \in \mathscr{f}^{(m)}(E)\), where \(E\) is an open subset of \(R^{n}\). Fix a \(\in E\), and suppose \(x \in R^{n}\) is so close to 0 that the points $$ \mathbf{p}(t)=\mathbf{a}+\boldsymbol{r} \mathbf{x} $$ lie in \(E\) whenever \(0 \leq t \leq 1\). Define $$ h(t)=f(\mathrm{p}(t)) $$ for all \(t \in R^{1}\) for which \(\mathrm{p}(t) \in E\). (a) For \(1 \leq k \leq m\), show (by repeated application of the chain rule) that $$ h^{(k)}(t)=\sum\left(D_{i_{1} \ldots i_{k}} f\right)(\mathrm{p}(t)) x_{i_{1} \ldots} x_{i_{k}} . $$ The sum extends over all ordered \(k\) -tuples \(\left(i_{1}, \ldots, i_{k}\right)\) in which each \(i\), is one of the integers \(1, \ldots, n\). (b) By Taylor's theorem (5.15), $$ h(1)=\sum_{k=0}^{m-1} \frac{h^{(k)}(0)}{k !}+\frac{h^{(m)}(t)}{m !} $$ for some \(t \in(0,1) .\) Use this to prove Taylor's theorem in \(n\) variables by showing that the formula $$ f(\mathbf{a}+\mathbf{x})=\sum_{k=0}^{m-1} \frac{1}{k !} \sum\left(D_{i 1} \ldots, f\right)(\mathbf{a}) x_{i_{1}} \ldots x_{1,}+r(\mathbf{x}) $$ represents \(f(\mathrm{a}+\mathbf{x})\) as the sum of its so-called "Taylor polynomial of degree \(m-1, "\) plus a remainder that satisfies $$ \lim _{x \rightarrow 0} \frac{r(\mathbf{x})}{|x|=-1}=0 $$ Each of the inner sums extends over all ordered \(k\) -tuples \(\left(i_{1}, \ldots, i_{k}\right)\), as in part \((a)\); as usual, the zero-order derivative of \(f\) is simply \(f\), so that the constant term of the Taylor polynomial of \(f\) at a is \(f(\mathbf{a})\). (c) Exercise 29 shows that repetition occurs in the Taylor polynomial as written in part ( \(b\) ). For instance, \(D_{113}\) occurs three times, as \(D_{113}, D_{131}, D_{311}\). The sum of the corresponding three terms can be written in the form $$ 3\left(D_{1}^{2} D_{3} f\right)(a) x_{1}^{2} x_{3} $$ Prove (by calculating how often each derivative occurs) that the Taylor polynomial in \((b)\) can be written in the form $$ \sum \frac{\left(D_{1}^{21} \cdots D_{n}^{s n} f\right)(a)}{s_{1} ! \cdots s_{n} !} x_{1}^{21} \cdots x_{n}^{s_{n}} $$ Here the summation extends over all ordered \(n\) -tuples \(\left(s_{1}, \ldots, s_{n}\right)\) such that each \(s_{1}\) is a nonnegative integer, and \(s_{1}+\cdots+s_{n} \leq m-1\).

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