Manifolds - Charts on Real Projective Spaces

In summary, Peter says that the map \phi_i \circ \pi is well defined because its value is unchanged by multiplying x by a nonzero constant. The domain of \phi_i is an equivalence class consisting of all points in \mathbb{R}^{n+1} in the same 1D subspace as ( x^1, \ ... \ ... \ , x^{n+1} ).
  • #1
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I am reading John M. Lee's book: Introduction to Smooth Manifolds ...

I am focused on Chapter 1: Smooth Manifolds ...

I need some help in fully understanding Example 1.3: Projective Spaces ... ...

Example 1.3 reads as follows:
?temp_hash=34a4a947f7de95ebf30378e636b4ee5e.png
My questions are as follows:Question 1In the above example, we read:

" ... ... define a map [itex]\phi_i \ : \ U_i \longrightarrow \mathbb{R}^n[/itex] by[itex] \phi_i [ x^1, \ ... \ ... \ , x^{n+1} ] = ( \frac{x^1}{x^i} , \ ... \ , \frac{x^{i-1}}{x^i} , \frac{x^{i+1}}{x^i}, \ ... \ , \frac{x^{n+1}}{x^i} )[/itex]This map is well defined because its value is unchanged by multiplying x by a nonzero constant. ... ... "Now, in the above, the domain of [itex]\phi_i[/itex] is shown as an [itex](n+1)[/itex]-dimensional point ... ... BUT ... ... [itex]\phi_i[/itex] is a map with a domain consisting of lines in [itex]\mathbb{R}^{n + 1}[/itex], so shouldn't the dimension of the domain be [itex]n[/itex] ... ?

Maybe we have to regard the equivalence classes of the quotient topology involved as [itex](n+1)[/itex]-dimensional points and recognise that points [itex]x = \lambda x[/itex] where [itex]\lambda \in \mathbb{R}[/itex] ... ... is that right?

(The statement about the map being well defined is presumably about recognising equivalence classes as on point in the projective space ... ... is that right? ... ...)

==========================================================

Question 2In the above text from Lee's book we read:

"... ... Because [itex]\phi_i \circ \pi[/itex] is continuous ... ... "How do we know that [itex]\phi_i \circ \pi[/itex] is continuous ... ?

============================================================

Question 3

In the above text from Lee's book we read:

"... ... In fact [itex]\phi_i[/itex] is a homeomorphism, because its inverse is given by

[itex]{\phi_i}^{-1} [ u^1, \ ... \ ... \ , u^{n} ] = [ u^1, \ ... u^{i-1}, 1, u^i, \ ... \ , u^{n} ][/itex]

as you can easily check ... ... "
I cannot see how Lee determined this expression to be the inverse ... why is the inverse of [itex]\phi_i[/itex] of the form shown ... how do we get this expression ... and why is it continuous (as it must be since Lee declares [itex]\phi_i[/itex] to be a homeomorphism ... ...

===========================================================================

Question 4

Just a general question ... in seeking a set of charts to cover [itex]\mathbb{RP}^n[/itex], why does Lee bother with the [itex]\tilde{U_i}[/itex] and [itex]\pi[/itex] ... why not just define the [itex]U_i[/itex] as an open set of [itex]\mathbb{RP}^n[/itex] and define the [itex]\phi_i[/itex] ... ... ?

============================================================================Hope someone can help with the above three questions ...

Help will be appreciated ... ...

Peter
 

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  • #2
I'll do one now (the easiest one, of course), then come back later and have a look at the others.
Math Amateur said:
Question 1In the above example, we read:

" ... ... define a map [itex]\phi_i \ : \ U_i \longrightarrow \mathbb{R}^n[/itex] by[itex] \phi_i [ x^1, \ ... \ ... \ , x^{n+1} ] = ( \frac{x^1}{x^i} , \ ... \ , \frac{x^{i-1}}{x^i} , \frac{x^{i+1}}{x^i}, \ ... \ , \frac{x^{n+1}}{x^i} )[/itex]This map is well defined because its value is unchanged by multiplying x by a nonzero constant. ... ... "Now, in the above, the domain of [itex]\phi_i[/itex] is shown as an [itex](n+1)[/itex]-dimensional point ... ... BUT ... ... [itex]\phi_i[/itex] is a map with a domain consisting of lines in [itex]\mathbb{R}^{n + 1}[/itex], so shouldn't the dimension of the domain be [itex]n[/itex] ... ?
The operand of ##\phi_i## is ## [ x^1, \ ... \ ... \ , x^{n+1} ] ##, which is not an ##(n+1)##-dimensional point but an equivalence class consisting of all points in ##\mathbb{R}^{n+1}## in the same 1D subspace as ## ( x^1, \ ... \ ... \ , x^{n+1} ) ##. That is indicated by the use of square, rather than round brackets. See the last line of the first para of Example 1.3. Taking equivalence classes reduces the dimension from ##n+1## to ##n##.

They've abused notation a little, which may have confused you. They should have written ##\phi_i\big([(x^1, \ ... \ ... \ , x^{n+1} )]\big)## not ##\phi_i[ x^1, \ ... \ ... \ , x^{n+1} ]##, but they omitted both sets of round brackets.

I get the impression you may have guessed this from what you wrote next, but I wasn't sure what you were getting at there, so I wrote this.
 
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  • #3
Just one more quick one:
Math Amateur said:
Question 2In the above text from Lee's book we read:

"... ... Because [itex]\phi_i \circ \pi[/itex] is continuous ... ... "How do we know that [itex]\phi_i \circ \pi[/itex] is continuous ... ?
Peter
The map ##\phi_i\circ\pi## is much simpler than it looks, because the composition of the two removes most of the complexity involved in taking quotients. What the composed map does is simply (1) divide all components by the ##i##th component, which we know to be nonzero, then (2) drop the ##i##th component. Both of these steps are easily shown to be continuous by epsilon-delta arguments, and the composition of continuous functions - in this case the functions representing step 1 and step 2, not ##\phi_i## and ##\pi## - is continuous.
 
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  • #4
andrewkirk said:
I'll do one now (the easiest one, of course), then come back later and have a look at the others.

The operand of ##\phi_i## is ## [ x^1, \ ... \ ... \ , x^{n+1} ] ##, which is not an ##(n+1)##-dimensional point but an equivalence class consisting of all points in ##\mathbb{R}^{n+1}## in the same 1D subspace as ## ( x^1, \ ... \ ... \ , x^{n+1} ) ##. That is indicated by the use of square, rather than round brackets. See the last line of the first para of Example 1.3. Taking equivalence classes reduces the dimension from ##n+1## to ##n##.

They've abused notation a little, which may have confused you. They should have written ##\phi_i\big([(x^1, \ ... \ ... \ , x^{n+1} )]\big)## not ##\phi_i[ x^1, \ ... \ ... \ , x^{n+1} ]##, but they omitted both sets of round brackets.

I get the impression you may have guessed this from what you wrote next, but I wasn't sure what you were getting at there, so I wrote this.
Thanks for the help, Andrew

Peter
 
  • #5
andrewkirk said:
Just one more quick one:
The map ##\phi_i\circ\pi## is much simpler than it looks, because the composition of the two removes most of the complexity involved in taking quotients. What the composed map does is simply (1) divide all components by the ##i##th component, which we know to be nonzero, then (2) drop the ##i##th component. Both of these steps are easily shown to be continuous by epsilon-delta arguments, and the composition of continuous functions - in this case the functions representing step 1 and step 2, not ##\phi_i## and ##\pi## - is continuous.

Thanks again Andrew

Peter
 
  • #6
Math Amateur said:
Question 3

In the above text from Lee's book we read:

"... ... In fact [itex]\phi_i[/itex] is a homeomorphism, because its inverse is given by

[itex]{\phi_i}^{-1} [ u^1, \ ... \ ... \ , u^{n} ] = [ u^1, \ ... u^{i-1}, 1, u^i, \ ... \ , u^{n} ][/itex]

as you can easily check ... ... "
I cannot see how Lee determined this expression to be the inverse ... why is the inverse of [itex]\phi_i[/itex] of the form shown ... how do we get this expression ... and why is it continuous (as it must be since Lee declares [itex]\phi_i[/itex] to be a homeomorphism ... ...
Careful now! That's not exactly what Lee wrote.
What he wrote was:
[itex]{\phi_i}^{-1} (u^1, \ ... \ ... \ , u^{n}) = [ u^1, \ ... u^{i-1}, 1, u^i, \ ... \ , u^{n} ][/itex]
and what he should have written is:
[itex]{\phi_i}^{-1} \big(\ ( u^1, \ ... \ ... \ , u^{n} )\big) = [ u^1, \ ... u^{i-1}, 1, u^i, \ ... \ , u^{n} ][/itex]
The difference between your and his version is somewhat important, because I suspect the inverse function won't work if the element it's operating on is an equivalence class rather than an ordinary old point in ##\mathbb{R}^n##.

To check that it's an inverse, you just have to show that
[itex]\phi_i\left({\phi_i}^{-1} \left(\langle u^1, \ ... \ ... \ , u^{n} \rangle\right)\right) =\langle u^1, \ ... \ ... \ , u^{n} \rangle[/itex]
and
[itex]\phi_i^{-1}\left({\phi_i}\left(\left[\langle x^1, \ ... \ ... \ , x^{n+1} \rangle\right]\right)\right) = \left[\langle x^1, \ ... \ ... \ , x^{n+1} \rangle\right][/itex]

where I've used angle brackets to delimit points in Euclidean space, to avoid bracket overload.

Just apply the formulas for these two functions that Lee gives above, and these should work out.

For continuity of ##\phi_i^{-1}## it is enough to show that for any open ball ##B## in ##\mathbb{R}^n##, ##\phi^{-1}(B)## is open in ##\mathbb{RP}^{n}##. To do that, you'll need to think a bit about what open sets in ##\mathbb{RP}^n## look like, which is a useful exercise in itself. I suspect there's a quicker way of proving continuity by using ##\pi##, but in this case doing it the long way may have more lasting value.

Good luck!
 
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  • #7
hI Andrew,

Thanks for helping me to make progress on understanding the basics of differential topology ... really appreciate the support ...

Just working on what you suggested ...

Peter
 
  • #8
Might as well do the last one as well:
Math Amateur said:
Question 4

Just a general question ... in seeking a set of charts to cover [itex]\mathbb{RP}^n[/itex], why does Lee bother with the [itex]\tilde{U_i}[/itex] and [itex]\pi[/itex] ... why not just define the [itex]U_i[/itex] as an open set of [itex]\mathbb{RP}^n[/itex] and define the [itex]\phi_i[/itex] ... ... ?
The function ##\pi:\mathbb{R}^{n+1}\smallsetminus\{0\}\to\mathbb{RP}^{n}## is needed because to create a quotient space you need a quotient map. ##\pi## is that map. The continuity of ##\pi## is used to establish the continuity of each ##\phi_i##.

I like the ##\tilde{U}_i## sets because they are easier to visualise than their images ##U_i## in ##\mathbb{RP}^{n}##. THey provide an easy way to visualise open sets in ##\mathbb{RP}^n##. They may also be used in establishing continuity of various maps.
 
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  • #9
Thanks for your posts Andrew ... they have been most helpful ...

Peter
 

1. What is a manifold?

A manifold is a mathematical concept that describes a space that is locally similar to Euclidean space, meaning that it looks like a flat, infinite plane when zoomed in. Manifolds can have any number of dimensions and can be smooth or have sharp edges. They are often used in geometry, topology, and physics to study and describe complex spaces.

2. What are charts on a manifold?

Charts are mathematical tools used to map points on a manifold to points in Euclidean space. They consist of a coordinate system and a set of equations that describe the relationship between points on the manifold and points in Euclidean space. These charts allow us to visualize and work with manifolds in a more familiar and tangible way.

3. What is a real projective space?

A real projective space is a type of manifold that is formed by taking a Euclidean space and adding "points at infinity." In other words, it is a way of extending a Euclidean space to include points that are infinitely far away. Real projective spaces are often used in projective geometry and have applications in computer graphics, computer vision, and other fields.

4. How are charts used on real projective spaces?

Charts on real projective spaces are used to map points on the manifold to points in Euclidean space, just like on any other manifold. However, because real projective spaces have points at infinity, charts on these spaces are often more complex and require additional mathematical tools. These charts allow us to study and analyze the properties of real projective spaces in a more concrete way.

5. What are some real-world applications of manifolds and charts on real projective spaces?

Manifolds and charts on real projective spaces have a wide range of applications in fields such as computer graphics, computer vision, robotics, and physics. For example, they can be used to model and simulate complex objects, such as human faces, in computer graphics. In computer vision, they can be used to map images onto three-dimensional objects. In robotics, they can be used to plan and control the movements of robots. In physics, they can be used to study the behavior of particles in spacetime.

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