Is the hyperplane of equation [f=c] closed if and only if f is continuous?

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In summary, The hyperplane of equation [f=c] is closed if and only if f is continuous. This is shown by proving that if f is continuous, then the preimage of a closed set is also closed, and vice versa. One way to show this is by using the natural mapping and proving that f is continuous at zero/bounded about zero. Another approach is by choosing a point in the complement of the hyperplane and showing that there exists a ball centered on that point that is entirely contained in the complement, thus proving the continuity of f.
  • #1
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[SOLVED] hyperplanes and continuity

Homework Statement


Let X be a real normed linear space, f a linear functional on X and c a real constant. The set [itex]f^{-1}(c)[/itex] is called the hyperplane of equation [f=c] and supposedly, the hyperplane of equation [f=c] is closed if and only if f is continuous.

Is this obvious? I don't see it.


The Attempt at a Solution



The part <== is obvious: f is continuous so the preimage of a closed set is a closed set. Therfor, since {c} is a closet set, so is the hyperplane [itex]f^{-1}(c)[/itex].

What about the other direction?
 
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  • #2
Consider the natural mapping [itex]\pi : X \to X/f^{-1}(c)[/itex]. This is continuous (in fact contractive), surjective and linear. Let [itex]f^*:X/f^{-1}(c) \to \mathbb{R}[/itex] be the induced linear embedding from f. Note that [itex]f^* \circ \pi[/itex] is a linear functional on X whose kernel is [itex]f^{-1}(c)[/tex]. Conversely, the kernel of any linear functional is obviously a hyperplane. So it suffices to prove that f is continuous iff its kernel is closed.

Try doing this using the same ideas here. You'll find it useful to keep in mind that if two linear functionals have the same kernel, then one is a scalar multiple of the other.

If you don't like playing with quotient spaces, you can try showing that f is continuous at zero / bounded about zero.
 
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  • #3
Whether this is obvious or how you would prove it depends on what definitions you have to use. A standard definition for "continuous" is that a function f is continuous if and only if f-1(U) is open whenever U is open. From that it follows that f-1(V) is closed whenever V is closed (and sometimes that is used as the definition of "continuous"). That, together with the fact that a singleton set, such as {c} is closed in a Hilbert Space, does make this statement obvious!
 
  • #4
I don't think it's that simple Halls! That f^-1({c}) is closed if f is continuous is certainly obvious as you say, but going the other direction is definitely harder.
 
  • #5
To conclude that f is continuous, it must be that all closed sets have closed preimage, not just a singleton {c} !
 
  • #6
You're right. I misread that completely.
 
  • #7
My professor said "the hyper plane is close, so its complement is open. Chose a point in said complement. Then there is a ball centered on it a completely contained in said complement. Show that f is continuous at a.

Anyone sees how this argument works in the details? :confused:
 
  • #8
I asked her for details today.. it goes like this.

Call A the complement of the hyperplane of equation [f=c]. Since A is open, there exist an a in A and a ball of radius r centered on a and entirely contained in A. We can assume without loss of generality that f(a)<c. It must be also that all elements y in the ball satisfies f(y)<c (because if f(y)>c, by convexity, it must be that the ball intersects the hyperplane). This condition can be written as "for all ||z||<1, f(a+rz)<c". So by linearity, f(z)<(c-f(a))/r. Taking the sup over z, this gives the continuity of f.
 
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FAQ: Is the hyperplane of equation [f=c] closed if and only if f is continuous?

What are hyperplanes and how are they related to continuity?

Hyperplanes are high-dimensional analogues of planes in 3-dimensional space. They are defined as subspaces of a higher dimensional space that have one less dimension than the original space. In terms of continuity, hyperplanes play a crucial role in determining the continuity of a function, as they are used to define the boundary between two regions of a function's domain.

What is the significance of hyperplanes in machine learning and data analysis?

Hyperplanes are used in machine learning and data analysis to classify data points and separate them into different categories. This is known as linear classification, and hyperplanes are often used as decision boundaries to determine which category a data point belongs to.

How do you determine if a function is continuous using hyperplanes?

A function is considered continuous if the values of the function at points close to each other remain close. In terms of hyperplanes, this means that the function must remain on the same side of the hyperplane as the input values approach the boundary defined by the hyperplane. If the value of the function changes sides of the hyperplane, then it is not continuous at that point.

Can hyperplanes be used to approximate non-linear functions?

Yes, hyperplanes can be used to approximate non-linear functions through a process called kernelization. This involves mapping the original data to a higher dimensional space where it can be separated by a hyperplane, and then mapping it back to the original space. This allows for non-linear functions to be approximated by a linear hyperplane in a higher dimensional space.

What is the relationship between hyperplanes and linear independence?

Hyperplanes are defined by a set of linearly independent vectors. This means that the vectors cannot be expressed as a linear combination of each other. In order to determine the dimension of a hyperplane, the number of linearly independent vectors used to define it must be known.

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