Rank condition in the Implicit Mapping Theorm

In summary, the Implicit Mapping Theorm states that if a function has a maximum possible rank, then the level set of the function is locally a smooth manifold.
  • #1
cmiller5277
4
0
Hi there. I've recently come across the Implicit Mapping Theorm in my studies and noticed that there is a condition that the rank of the image must be the maximum possible. I'm not directly seeing why this condition is needed, so I was wondering if anyone could provide me with an example of why this condition cannot be dropped. Thanks
 
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  • #2
The simplest case of the theorem applies to a differentiable realvalued function on R^2, say F(y,x). Then if [tex]\partial F/\partial y \neq 0[/tex] at some point (y_0,x_0), there is a neighbourhood I of x_0 in R such that there exist a differentiable f:I -> R^2 with f(x_0)=y_0 and F(f(x),x)=0 for x in I.

Now consider for instance F(y,x)=x. Then F(y,x)=0 corresponds to the vertical x=0 line in the x,y-plane. Obviously this is not (locally) the graph of some function f(x). The reason for this is that the tangentlines are vertical, which is equivalent to [tex]\partial F/\partial y = 0[/tex]. So the condition is necessary.
 
  • #3
That does make sense, thank you.
 
  • #4
that is a bad example for the question asked. i.e. he asked about the implicit function theorem, and its relation to maximality of rank of the map.

the map here is F(x,y) and its rank is the rank of the matrix of partials with entries dF/dx and dF/dy.

the theorem says that if either of these partials is non zero at p, then the level set F= F(p) is locally a smooth manifold, i.e. the graph of a function, either y(x) or x(y), in your example x(y).

A real example where the theorem fails would be a function like F(x,y) = xy, where the gradient at the origin is (0,0), hence has rank zero, not 1.

the pair of lines xy=0 is not locally the graph of any function either of x or of y near (0,0).the correct hypothesis for the rank theorem however is not that the rank be maximal but that it be locally constant. note that since rank is upper semi continuous, rank maximal at a point, implies rank locally maximal hence rank locally constant.
 

What is the rank condition in the Implicit Mapping Theorem?

The rank condition in the Implicit Mapping Theorem refers to the condition that must be satisfied in order for the theorem to hold. It states that the derivative of the implicit function must have full rank, meaning that it must be invertible at the point in question.

Why is the rank condition important in the Implicit Mapping Theorem?

The rank condition is important because it ensures that the implicit function exists and is unique at the point in question. Without this condition, the implicit function may not be well-defined, making the theorem invalid.

How is the rank condition related to the Inverse Function Theorem?

The rank condition is closely related to the Inverse Function Theorem. In fact, the Inverse Function Theorem is a special case of the Implicit Mapping Theorem, where the implicit function is defined explicitly. Both theorems require the derivative of the function to be invertible at the point in question.

What happens if the rank condition is not satisfied in the Implicit Mapping Theorem?

If the rank condition is not satisfied, the implicit function may not exist or may not be unique at the point in question. This means that the theorem cannot be applied and we cannot guarantee the existence or uniqueness of the implicit function.

Can the rank condition be generalized to higher dimensions?

Yes, the rank condition can be generalized to higher dimensions. In higher dimensions, the derivative of the implicit function must have full rank at every point in the domain for the theorem to hold. This ensures that the implicit function is well-defined and unique at every point in the domain.

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