Implicit function theorem

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}} In multivariable calculus, the implicit function theoremTemplate:Efn is a tool that allows relations to be converted to functions of several real variables. It does so by representing the relation as the graph of a function. There may not be a single function whose graph can represent the entire relation, but there may be such a function on a restriction of the domain of the relation. The implicit function theorem gives a sufficient condition to ensure that there is such a function.

More precisely, given a system of Template:Mvar equations Template:Math (often abbreviated into Template:Math), the theorem states that, under a mild condition on the partial derivatives (with respect to each Template:Math ) at a point, the Template:Mvar variables Template:Math are differentiable functions of the Template:Math in some neighborhood of the point. As these functions generally cannot be expressed in closed form, they are implicitly defined by the equations, and this motivated the name of the theorem.<ref>Template:Cite book</ref>

In other words, under a mild condition on the partial derivatives, the set of zeros of a system of equations is locally the graph of a function.

HistoryEdit

Augustin-Louis Cauchy (1789–1857) is credited with the first rigorous form of the implicit function theorem. Ulisse Dini (1845–1918) generalized the real-variable version of the implicit function theorem to the context of functions of any number of real variables.<ref>Template:Cite book</ref>

Two variables caseEdit

Let <math>f:\R^2 \to \R</math> be a continuously differentiable function defining the implicit equation of a curve <math> f(x,y) = 0 </math>. Let <math>(x_0, y_0)</math> be a point on the curve, that is, a point such that <math>f(x_0, y_0)=0</math>. In this simple case, the implicit function theorem can be stated as follows: Template:Math theorem

Proof. By differentiating the equation Template:Tmath, one gets <math display=block>\frac{\partial f}{ \partial x}(x, \varphi(x))+\varphi'(x)\, \frac{\partial f}{ \partial y}(x, \varphi(x))=0. </math> and thus <math display=block>\varphi'(x)=-\frac{\frac{\partial f}{ \partial x}(x, \varphi(x))}{\frac{\partial f}{ \partial y}(x, \varphi(x))}.</math> This gives an ordinary differential equation for Template:Tmath, with the initial condition Template:Tmath.

Since <math display=inline>\frac{\partial f}{ \partial y} (x_0, y_0) \neq 0,</math> the right-hand side of the differential equation is continuous, upper bounded and lower bounded on some closed interval around Template:Tmath. It is therefore Lipschitz continuous,{{ safesubst:#invoke:Unsubst||date=__DATE__ |$B= Template:Fix }} and the Cauchy-Lipschitz theorem applies for proving the existence of a unique solution.

First exampleEdit

File:Implicit circle.svg

The unit circle of implicit equation Template:Math cannot be represented as the graph of a function. Around the point Template:Math where the tangent is not vertical, the bolded circular arc is the graph of some function of Template:Mvar, while around Template:Math, there is no function of Template:Mvar with the circle as its graph.
This is exactly what the implicit function theorem asserts in this case.

If we define the function Template:Math, then the equation Template:Math cuts out the unit circle as the level set Template:Math. There is no way to represent the unit circle as the graph of a function of one variable Template:Math because for each choice of Template:Math, there are two choices of y, namely <math>\pm\sqrt{1-x^2}</math>.

However, it is possible to represent part of the circle as the graph of a function of one variable. If we let <math>g_1(x) = \sqrt{1-x^2}</math> for Template:Math, then the graph of Template:Math provides the upper half of the circle. Similarly, if <math>g_2(x) = -\sqrt{1-x^2}</math>, then the graph of Template:Math gives the lower half of the circle.

The purpose of the implicit function theorem is to tell us that functions like Template:Math and Template:Math almost always exist, even in situations where we cannot write down explicit formulas. It guarantees that Template:Math and Template:Math are differentiable, and it even works in situations where we do not have a formula for Template:Math.

DefinitionsEdit

Let <math>f: \R^{n+m} \to \R^m</math> be a continuously differentiable function. We think of <math>\R^{n+m}</math> as the Cartesian product <math>\R^n\times\R^m,</math> and we write a point of this product as <math>(\mathbf{x}, \mathbf{y}) = (x_1,\ldots, x_n, y_1, \ldots y_m).</math> Starting from the given function <math>f</math>, our goal is to construct a function <math>g: \R^n \to \R^m</math> whose graph <math>(\textbf{x}, g(\textbf{x}))</math> is precisely the set of all <math>(\textbf{x}, \textbf{y})</math> such that <math>f(\textbf{x}, \textbf{y}) = \textbf{0}</math>.

As noted above, this may not always be possible. We will therefore fix a point <math>(\textbf{a}, \textbf{b}) = (a_1, \dots, a_n, b_1, \dots, b_m)</math> which satisfies <math>f(\textbf{a}, \textbf{b}) = \textbf{0}</math>, and we will ask for a <math>g</math> that works near the point <math>(\textbf{a}, \textbf{b})</math>. In other words, we want an open set <math>U \subset \R^n</math> containing <math>\textbf{a}</math>, an open set <math>V \subset \R^m</math> containing <math>\textbf{b}</math>, and a function <math>g : U \to V</math> such that the graph of <math>g</math> satisfies the relation <math>f = \textbf{0}</math> on <math>U\times V</math>, and that no other points within <math>U \times V</math> do so. In symbols,

<math display="block">\{ (\mathbf{x}, g(\mathbf{x})) \mid \mathbf x \in U \} = \{ (\mathbf{x}, \mathbf{y})\in U \times V \mid f(\mathbf{x}, \mathbf{y}) = \mathbf{0} \}.</math>

To state the implicit function theorem, we need the Jacobian matrix of <math>f</math>, which is the matrix of the partial derivatives of <math>f</math>. Abbreviating <math>(a_1, \dots, a_n, b_1, \dots, b_m)</math> to <math>(\textbf{a}, \textbf{b})</math>, the Jacobian matrix is

<math display="block">(Df)(\mathbf{a},\mathbf{b}) = \left[\begin{array}{ccc|ccc}

\frac{\partial f_1}{\partial x_1}(\mathbf{a},\mathbf{b}) & \cdots & \frac{\partial f_1}{\partial x_n}(\mathbf{a},\mathbf{b}) &
\frac{\partial f_1}{\partial y_1}(\mathbf{a},\mathbf{b}) & \cdots & \frac{\partial f_1}{\partial y_m}(\mathbf{a},\mathbf{b}) \\
\vdots & \ddots & \vdots & \vdots & \ddots & \vdots \\
\frac{\partial f_m}{\partial x_1}(\mathbf{a},\mathbf{b}) & \cdots & \frac{\partial f_m}{\partial x_n}(\mathbf{a},\mathbf{b}) &
\frac{\partial f_m}{\partial y_1}(\mathbf{a},\mathbf{b}) & \cdots & \frac{\partial f_m}{\partial y_m}(\mathbf{a},\mathbf{b})

\end{array}\right] = \left[\begin{array}{c|c} X & Y \end{array}\right]</math>

where <math>X</math> is the matrix of partial derivatives in the variables <math>x_i</math> and <math>Y</math> is the matrix of partial derivatives in the variables <math>y_j</math>. The implicit function theorem says that if <math>Y</math> is an invertible matrix, then there are <math>U</math>, <math>V</math>, and <math>g</math> as desired. Writing all the hypotheses together gives the following statement.

Statement of the theoremEdit

Let <math>f: \R^{n+m} \to \R^m</math> be a continuously differentiable function, and let <math>\R^{n+m}</math> have coordinates <math>(\textbf{x}, \textbf{y})</math>. Fix a point <math>(\textbf{a}, \textbf{b}) = (a_1,\dots,a_n, b_1,\dots, b_m)</math> with <math>f(\textbf{a}, \textbf{b}) = \mathbf{0}</math>, where <math>\mathbf{0} \in \R^m</math> is the zero vector. If the Jacobian matrix (this is the right-hand panel of the Jacobian matrix shown in the previous section): <math display="block">J_{f, \mathbf{y}} (\mathbf{a}, \mathbf{b}) = \left [ \frac{\partial f_i}{\partial y_j} (\mathbf{a}, \mathbf{b}) \right ]</math> is invertible, then there exists an open set <math>U \subset \R^n</math> containing <math>\textbf{a}</math> such that there exists a unique function <math>g: U \to \R^m</math> such that Template:Nowrap and Template:Nowrap Moreover, <math>g</math> is continuously differentiable and, denoting the left-hand panel of the Jacobian matrix shown in the previous section as: <math display="block"> J_{f, \mathbf{x}} (\mathbf{a}, \mathbf{b}) = \left [ \frac{\partial f_i}{\partial x_j} (\mathbf{a}, \mathbf{b}) \right ], </math> the Jacobian matrix of partial derivatives of <math>g</math> in <math>U</math> is given by the matrix product:<ref>Template:Cite journal</ref> <math display="block"> \left[\frac{\partial g_i}{\partial x_j} (\mathbf{x})\right]_{m\times n} =- \left [ J_{f, \mathbf{y}}(\mathbf{x}, g(\mathbf{x})) \right ]_{m \times m} ^{-1} \, \left [ J_{f, \mathbf{x}}(\mathbf{x}, g(\mathbf{x})) \right ]_{m \times n} </math>

For a proof, see Inverse function theorem#Implicit_function_theorem. Here, the two-dimensional case is detailed.

Higher derivativesEdit

If, moreover, <math>f</math> is analytic or continuously differentiable <math>k</math> times in a neighborhood of <math>(\textbf{a}, \textbf{b})</math>, then one may choose <math>U</math> in order that the same holds true for <math>g</math> inside <math>U</math>. <ref>Template:Cite book</ref> In the analytic case, this is called the analytic implicit function theorem.

The circle exampleEdit

Let us go back to the example of the unit circle. In this case n = m = 1 and <math>f(x,y) = x^2 + y^2 - 1</math>. The matrix of partial derivatives is just a 1 × 2 matrix, given by <math display="block">(Df)(a,b) = \begin{bmatrix} \dfrac{\partial f}{\partial x}(a,b) & \dfrac{\partial f}{\partial y}(a,b) \end{bmatrix} = \begin{bmatrix} 2a & 2b \end{bmatrix}</math>

Thus, here, the Template:Math in the statement of the theorem is just the number Template:Math; the linear map defined by it is invertible if and only if Template:Math. By the implicit function theorem we see that we can locally write the circle in the form Template:Math for all points where Template:Math. For Template:Math we run into trouble, as noted before. The implicit function theorem may still be applied to these two points, by writing Template:Mvar as a function of Template:Mvar, that is, <math>x = h(y)</math>; now the graph of the function will be <math>\left(h(y), y\right)</math>, since where Template:Math we have Template:Math, and the conditions to locally express the function in this form are satisfied.

The implicit derivative of y with respect to x, and that of x with respect to y, can be found by totally differentiating the implicit function <math>x^2+y^2-1</math> and equating to 0: <math display="block">2x\, dx+2y\, dy = 0,</math> giving <math display="block">\frac{dy}{dx}=-\frac{x}{y}</math> and <math display="block">\frac{dx}{dy} = -\frac{y}{x}. </math>

Application: change of coordinatesEdit

Suppose we have an Template:Mvar-dimensional space, parametrised by a set of coordinates <math> (x_1,\ldots,x_m) </math>. We can introduce a new coordinate system <math> (x'_1,\ldots,x'_m) </math> by supplying m functions <math> h_1\ldots h_m </math> each being continuously differentiable. These functions allow us to calculate the new coordinates <math> (x'_1,\ldots,x'_m) </math> of a point, given the point's old coordinates <math> (x_1,\ldots,x_m) </math> using <math> x'_1=h_1(x_1,\ldots,x_m), \ldots, x'_m=h_m(x_1,\ldots,x_m) </math>. One might want to verify if the opposite is possible: given coordinates <math> (x'_1,\ldots,x'_m) </math>, can we 'go back' and calculate the same point's original coordinates <math> (x_1,\ldots,x_m) </math>? The implicit function theorem will provide an answer to this question. The (new and old) coordinates <math>(x'_1,\ldots,x'_m, x_1,\ldots,x_m)</math> are related by f = 0, with <math display="block">f(x'_1,\ldots,x'_m,x_1,\ldots, x_m)=(h_1(x_1,\ldots, x_m)-x'_1,\ldots , h_m(x_1,\ldots, x_m)-x'_m).</math> Now the Jacobian matrix of f at a certain point (a, b) [ where <math>a=(x'_1,\ldots,x'_m), b=(x_1,\ldots,x_m)</math> ] is given by <math display="block">(Df)(a,b) = \left [\begin{matrix}

-1 & \cdots & 0 \\
\vdots & \ddots & \vdots \\
0 & \cdots & -1

\end{matrix}\left| \begin{matrix} \frac{\partial h_1}{\partial x_1}(b) & \cdots & \frac{\partial h_1}{\partial x_m}(b)\\ \vdots & \ddots & \vdots\\ \frac{\partial h_m}{\partial x_1}(b) & \cdots & \frac{\partial h_m}{\partial x_m}(b)\\ \end{matrix} \right.\right] = [-I_m |J ].</math> where I_m denotes the m × m identity matrix, and Template:Mvar is the Template:Math matrix of partial derivatives, evaluated at (a, b). (In the above, these blocks were denoted by X and Y. As it happens, in this particular application of the theorem, neither matrix depends on a.) The implicit function theorem now states that we can locally express <math> (x_1,\ldots,x_m) </math> as a function of <math> (x'_1,\ldots,x'_m) </math> if J is invertible. Demanding J is invertible is equivalent to det J ≠ 0, thus we see that we can go back from the primed to the unprimed coordinates if the determinant of the Jacobian J is non-zero. This statement is also known as the inverse function theorem.

Example: polar coordinatesEdit

As a simple application of the above, consider the plane, parametrised by polar coordinates Template:Math. We can go to a new coordinate system (cartesian coordinates) by defining functions Template:Math and Template:Math. This makes it possible given any point Template:Math to find corresponding Cartesian coordinates Template:Math. When can we go back and convert Cartesian into polar coordinates? By the previous example, it is sufficient to have Template:Math, with <math display="block">J =\begin{bmatrix}

\frac{\partial x(R,\theta)}{\partial R} & \frac{\partial x(R,\theta)}{\partial \theta} \\
\frac{\partial y(R,\theta)}{\partial R} & \frac{\partial y(R,\theta)}{\partial \theta} \\

\end{bmatrix}=

\begin{bmatrix}
\cos \theta & -R \sin \theta \\
\sin \theta & R \cos \theta

\end{bmatrix}.</math> Since Template:Math, conversion back to polar coordinates is possible if Template:Math. So it remains to check the case Template:Math. It is easy to see that in case Template:Math, our coordinate transformation is not invertible: at the origin, the value of θ is not well-defined.

GeneralizationsEdit

Banach space versionEdit

Based on the inverse function theorem in Banach spaces, it is possible to extend the implicit function theorem to Banach space valued mappings.<ref>Template:Cite book</ref><ref>Template:Cite book</ref>

Let X, Y, Z be Banach spaces. Let the mapping Template:Math be continuously Fréchet differentiable. If <math>(x_0,y_0)\in X\times Y</math>, <math>f(x_0,y_0)=0</math>, and <math>y\mapsto Df(x_0,y_0)(0,y)</math> is a Banach space isomorphism from Y onto Z, then there exist neighbourhoods U of x₀ and V of y₀ and a Fréchet differentiable function g : U → V such that f(x, g(x)) = 0 and f(x, y) = 0 if and only if y = g(x), for all <math>(x,y)\in U\times V</math>.

Implicit functions from non-differentiable functionsEdit

Various forms of the implicit function theorem exist for the case when the function f is not differentiable. It is standard that local strict monotonicity suffices in one dimension.<ref>Template:Springer</ref> The following more general form was proven by Kumagai based on an observation by Jittorntrum.<ref>Template:Cite journal</ref><ref>Template:Cite journal</ref>

Consider a continuous function <math>f : \R^n \times \R^m \to \R^n</math> such that <math>f(x_0, y_0) = 0</math>. If there exist open neighbourhoods <math>A \subset \R^n</math> and <math>B \subset \R^m</math> of x₀ and y₀, respectively, such that, for all y in B, <math>f(\cdot, y) : A \to \R^n</math> is locally one-to-one, then there exist open neighbourhoods <math>A_0 \subset \R^n</math> and <math>B_0 \subset \R^m</math> of x₀ and y₀, such that, for all <math>y \in B_0</math>, the equation f(x, y) = 0 has a unique solution <math display="block">x = g(y) \in A_0,</math> where g is a continuous function from B₀ into A₀.

Collapsing manifoldsEdit

Perelman’s collapsing theorem for 3-manifolds, the capstone of his proof of Thurston's geometrization conjecture, can be understood as an extension of the implicit function theorem.<ref>Template:Cite journal</ref>

NotesEdit

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ReferencesEdit

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