The Organic Math of Origami

Did you know that there exists a whole set of seven axioms of Origami Geometry just like that of the Euclidean Geometry?

Related image
The Origami in building the solar panel maximizing the input of Solar Power and minimizing the Volume of the satellite

Instead of being very mathematically strict, today we will go through a very elegant result that arises organically from Origami.

Before that, let us travel through some basic terminologies. Be patient for a few more minutes and wait for the gem to arrive.

In case you have forgotten what Origami is, the following pictures will remove the dust from your memories.

Origami Dinosaurs
Origami Papers

Origami (from the Japanese oru, “to fold,” and kami, “paper”) is a traditional Japanese art of folding a sheet of paper, usually square, into a representation of an object such as a bird or flower.

Flat origami refers to configurations that can be pressed flat, say between the pages of a book, without adding any new folds or creases.

Non- Flat Origami
Image result for flat origami
Flat Origami

When an origami object is unfolded, the resulting diagram of folds or creases on the paper square is called a crease pattern.

We denote mountain folds by unbroken lines and valley folds by dashed
lines. A vertex of a crease pattern is a point where two or more folds intersect, and a flat vertex fold is a crease pattern with just one vertex.

The Origami and the Crease Pattern

In a crease pattern, we see two types of folds, called mountain folds and valley folds.


Related image

Now, if you get to play with your hands, you will get to discover a beautiful pattern.

Related image
The blue lines denotes the mountain folds and the red lines indicate the valley folds.

The positive difference between the dotted lines and the full lines is always 2 at a given vertex. The dotted line denotes the mountain fold and the full line denotes the valley fold.

This is encoded in the following theorem.

Maekawa's Theorem: The difference between the number of mountain
folds and the number of valley folds in a flat vertex fold is two.

Isn't it strange ?

Those who are familiar graph theory may think it is related to the Euler Number.

We will do the proof step by step but you will weave together the steps to understand it yourself. The proof is very easy.

Step 1:

Let n denote the number of folds that meet at the vertex, m of which
are mountain folds and v that are valley folds, so that n = m + v. (m for mountain folds and v for valley folds.)

Step 2:

Consider the cross section of a flat vertex.

Cross Section of a Flat Vertex

Step 3:

Consider the creases as shown and fold it accordingly depending on the type of fold - mountain or valley.

Folding the Cross Section of a Flat Vertex along the creases

Step 4:

Now observe that we get the following cross section. Observe that the number of sides of the formed polygon is n = m + v.


Step 5:

We will also count the angle sum of the n sided polygon in the following way. Consider the polygon formed.

We get a four sided polygon here with internal angles 0 degree and 360 degrees.
An upper view of the four sided triangle formed.

Observe that the vertex 2 is the Valley Fold and other vertices are Mountain Fold. Also the angle subtended the vertex due to valley fold is 360 degrees and that of due to the mountain fold is 0 degrees.

Therefore, the sum of the internal angles is v.360 degrees.

Step 6:

Now we also know that the sum of internal angles formed by n vertices is 180.(n-2), which is = v.360.

Hence we get by replacing n by m+v, that m - v = 2.

QED

So simple and yet so beautiful and magical right?

But it is just the beginning!

There are lot more to discover ...

Do you observe any pattern or any different symmetry about the creases or even in other geometry while playing with just paper and folding them?

We will love to hear it from you in the comments.

Do you know Cheenta is bringing out their third issue of the magazine "Reason, Debate and Story" this summer?

Do you want to write an article for us?

Email us at babinmukherjee08@gmail.com.







Natural Geometry of Natural Numbers

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How does this sound?

The numbers 18 and 30 together looks like a chair. 

The Natural Geometry of Natural Numbers is something that is never advertised, rarely talked about. Just feel how they feel!

Let's revise some ideas and concepts to understand the natural numbers more deeply.

We know by Unique Prime Factorization Theorem that every natural number can be uniquely represented by the product of primes.

So, a natural number is entirely known by the primes and their powers dividing it.

Also if you think carefully the entire information of a natural number is also entirely contained in the set of all of its divisors as every natural number has a unique set of divisors apart from itself.

We will discover the geometry of a natural number by adding lines between these divisors to form some shape and we call that the natural geometry corresponding to the number.

Let's start discovering by playing a game.

Take a natural number n and all its divisors including itself.

Consider two divisors a < b of n. Now draw a line segment between a and b based on the following rules:

Also write the number \(\frac{b}{a}\) over the line segment joining a and b.

Let's draw for number 6.

Number 6 has the shape like a square

Now, whatever shape we get, we call it the natural geometry of that particular number. Here we call that 6 has a natural geometry of a square or a rectangle. I prefer to call it a square because we all love symmetry.

What about all the numbers? Isn't interesting to know the geometry of all the natural numbers?

Let's draw for some other number say 30.


The Natural Geometry of 30 - A Cube

Observe this carefully, 30 has a complicated structure if seen in two dimensions but its natural geometrical structure is actually like a cube right?

The red numbers denote the divisors and the black numbers denote the numbers to be written on the line segment.

Beautiful right!

Have you observed something interesting?

Actually, it shows that to build this shape the requirement of the line segments is as important as the prime numbers to build the number.

Exercise: Prove from the rules of the game that the numbers on the line segment always correspond to prime numbers.

Did you observe this?

Exercise: Prove that the numbers corresponding to the parallel lines always have the same prime number on it.

Actually each prime number corresponds to a different direction. If you draw it perpendicularly we get the natural geometry of the number.

Let's observe the geometry of other numbers.

Try to draw the geometry of the number 210. It will look like the following:

Image result for Is four dimensional hyper cube graph

The natural geometry of the number 210 - Tesseract
Image result for tesseract cube
This is the three dimensional projection of the structure of the Tesseract.

Obviously, this is not the natural geometry as shown. But neither we can visualize it. The number 210 lies in four dimensions. If you try to discover this structure, you will find that it has four different directions corresponding to four different primes dividing it. Also, you will see that it is actually a four-dimensional cube, which is called a tesseract. What you see above is a two dimensional projection of the tesseract, we call it a graph.

A person acquainted with graph theory can understand that the graph of a number is always k- regular where k is the number of primes dividing the number.

Now it's time for you to discover more about the geometry of all the numbers.

I leave some exercises to help you along the way.

Exercise: Show that the natural geometry of \(p^k\) is a long straight line consisting of k small straight lines, where p is a prime number and k is a natural number.

Exercise: Show that all the numbers of the form \(p.q\) where p and q are two distinct prime numbers always have the natural geometry of a square.

Exercise: Show that all the numbers of the form \(p.q.r\) where p, q and r are three distinct prime numbers always have the natural geometry of a cube.

Research Exercise: Find the natural geometry of the numbers of the form \(p^2.q\) where p and q are two distinct prime numbers. Also, try to generalize and predict the geometry of \(p^k.q\) where k is any natural number.

Research Exercise: Find the natural geometry of \(p^a.q^b.r^c\) where p,
q, and r are three distinct prime numbers and a,b and c are natural numbers.

Let's end with the discussion with the geometry of {18, 30}. First let us define what I mean by it.

We define the natural geometry of two natural numbers quite naturally as a natural extension from that of a single number.

Take two natural numbers a and b. Consider the divisors of both a and b and follow the rules of the game on the set of divisors of both a and b. The shape that we get is called the natural geometry of {a, b}.

You can try it yourself and find out that the natural geometry of {18, 30} looks like the following:

Looks like a chair indeed!

Sit on this chair, grab a cup of coffee and set off to discover.

The numbers are eagerly waiting for your comments. 🙂

Please mention your observations and ideas and the proofs of the exercises in the comments section. Also think about what type of different shapes can we get from the numbers.

Also visit: Thousand Flowers Program

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Dudeney Puzzle: A Tale from Pythagoras to Dehn - Part II

(Remember the Dudeney puzzle introduced in the last post. We have ended with the question "Why four?"...We will be revealing the reason in this post.)

Obviously, by the way, we have given the algorithm, there is an upper bound to the number of pieces required.

The next natural question is Do there exist a lower bound?

What is the minimum number of cuts required to convert an equilateral triangle into a square?

Dudeney proved that 4 is the least he can reduce it to by the following elegant construction:

Now, this seems to arise the following question:

(1). Do there exist dissections with three pieces?

(2). Do there exist dissections with two pieces?

(1) is still unsolved, but (2) cannot take place and is obvious!

A Square \(\Leftrightarrow \) An Equilateral Triangle in two cuts is not possible:

The possible types of cuts are drawn above. Observe S3 cannot give rise to any of the T cuts as it gives rise to 2 quadrilaterals. S2 can give rise to T2 which is not possible S2 gives rise to a quadrilateral with 2 right angles. S1 can give rise to T1 but it is possible if T1 is the median cut but in that case, it will not be right-angled isosceles.

What about three pieces? Please share your ideas in the comments!

Let’s dive deep into the Four Piece Cut: A lot of work has been done in the past.

These are the details and yet ugly looking but it reveals how to do the cut.

Let me provide the math and measurements of one angle as it consists of one parameter only.

\(\alpha =\)arc\(\sin (\frac{\sqrt{\sqrt{3}}}{2}) \approx 41.150335^\circ\)

Well,  you will observe while solving this you will get a four-degree polynomial which has the shown above the real root. Also, perhaps, I am not sure this angle is not constructable, but for practical purposes to enjoy this six decimal place precision is perfect.

Since then these have become a good way for math entertainment in class to show the magical beauty of math.

Hey, this is not over!  We have more questions to ask and seek an answer to!

What is a general good lower bound on the number of pieces required to transform any polygon to another of the same area by the above method?

Alfred Tarski proved that if P is convex and the diameters of P and Q are respectively given by d(P)and d(Q), then the minimum number n of pieces required to compose polygon Q from another polygon P \(\geq \) d(P)/d(Q)

Can You prove it?

Think of this, it is very intuitive it means that for P \(\rightarrow \) Q with an increase in the maximum distance between two points in P and a decrease in that of Q we have to increase the number of pieces as we have to make triangles with a lesser diameter in a larger diameter polygon.

Thus for two convex polygons P and Q such that P \(\Leftrightarrow \) Q. Then the minimum number n of pieces required to compose polygon Q from another polygon P \(\geq \) max {d(P)/d(Q), d(Q)/d(P)}.

Since Alfred Tarski has arrived into the picture, we can’t help but sharing the idea about his dissection of a circle into a square.

Circle to Square Dissection Problem

The Problem:  Can you cut the circle into a finite number of pieces and reassemble them to get a square?[equidissectability]

Yes! They are not possible with scissor cut only! Even if curved edges are considered by scissor cuts. The proof requires a bit of playing around with pictures a beautiful observation. Remember the picture for Scissor Congruence:

Observe that the In scissors congruence, any time a section of the convex circular perimeter is created or destroyed it cancels with a corresponding pieces of concave circular perimeter. So convex circular perimeter − concave circular perimeter is an invariant of scissors congruence.


A 1964 publication by Dubins, Hirsch, and Karush informs us that a circular disk is “scissor congruent” to no other strictly convex body. We can never physically achieve a solution to the circle-squaring problem with scissors and paper. The proof used the above idea.

Now the beauty of something outside of Human Imagination comes into the question:

Can you decompose the circle into a finite number of pieces and reassemble them to get square?[equidecomposability, it means it may not be cut be scissors but yes it can be done somehow]

Miklós Laczkovich, however, shocked mathematicians around the world with an affirmative response to Tarski’s question. In 1990, Laczkovich proved that any circle in the plane is equidecomposable with a square of equal area. He succeeded because he allowed pieces that are difficult to imagine—dustings of points that are selected using the controversial Axiom of Choice. Laczkovich’s proof shows that such a decomposition is theoretically possible, but there is no picture to help us understand how this is accomplished. He gives an upper bound of \(10^{50}\) for the number of pieces that are required in this decomposition, and he shows that the rearrangement of the pieces can be accomplished using translations alone. None of the pieces require a rotation or a reflection.

Now, how about transcending into the third dimension?

Hilbert’s 3rd Problem exactly deals with this problem:

Given any two polyhedra of equal volume, is it always possible to cut the first into finitely many polyhedral pieces which can be reassembled to yield the second?

Max Dehn proved that it is false providing a counterexample using some Mathematical Idea called Dehn Invariant, which remains same under dissections and reassembling.

He proved that A cube and a regular tetrahedron are not dissection congruent.

In fact, the following result is true: Two polyhedra are dissection congruent if and only if they have the same volume and Dehn invariant.

Banach Tarski’s Paradox is worth mentioning and also is the child of Alfred Tarski, which is also based on the “decomposability” of the Sphere and is the starting point of measure theory and something out of human imagination and intuition:

Given a solid ball in 3‑dimensional space, there exists a decomposition of the ball into a finite number of disjoint subsets, which can then be put back together in a different way to yield two identical copies of the original ball. Indeed, the reassembly process involves only moving the pieces around and rotating them without changing their shape. However, the pieces themselves are not "solids" in the usual sense, but infinite scatterings of points. The reconstruction can work with as few as five pieces.

Dudeney Puzzle : A Tale from Pythagoras to Dehn

" Take care of yourself, you're not made of steel.
The fire has almost gone out and it is winter.
It kept me busy all night.
Excuse me, I will explain it to you.
You play this game, which is said to hail from China.
And I tell you that what Paris needs right now is to
welcome that which comes from far away. "

et's travel back 2 Millenia to the Land of the Greeks where Plato, Pythagoras, Archimedes devoted their life to Math and Science to understand and enjoy nature. Math was a Puzzle to them and they enjoyed doing it as we do it now. Pythagoras gave birth to the beautiful proof of Pythagoras Theorem just using pictures.

This approach of dissection and rearrangement was not new to him as the tradition of these sort of puzzles can be traced back to Plato and also continued by Archimedes.

Square trisection: Three squares to One square

       Square trisection: Three squares to One square

Two equal squares are turned into one square in fourteen pieces by subdivisions of the previous four pieces by Archimedes

Let’s Fast Forward to 20th Century

A puzzle became famous in 1903 as Henry Ernest Dudeney solved the Haberdasher's Puzzle.

The Puzzle:
Cut an equilateral triangle into four pieces that can be rearranged to make a square [Cut means by scissors so the result will be a set of  polygons]

An Obvious Question:

Wallace–Bolyai–Gerwien theorem: A Polygon can be cut into a finite number of pieces and then by rotations, reflections and translations(isometric transformations) can be reassembled into another Polygon iff both the polygons have the same area. We call this equidecomposability of two polygons of the same area. [Actually, it is called equidissectabilty, but then it is equidecomposability]

Well, this seems to be a beautiful piece of truth!

Let’s think about it a bit more!

Suppose you are given two polygons of the same area made of paper and a pair of scissors. You have in some sense no limit of cuts. How will you start cutting one of them?

First, observe that the intersection of two polygons is a polygon. Also, Union of polygons along the edges is also a polygon.

Now, you take two of them, place on above another, cut out the common portion by scissors and then reassemble the remaining pieces again and assemble them to form two different polygons of the same area and the problem reduces to the same problem, now if you are sure this process is going to end after some time, then we are done. This sort of congruence has a name “scissor congruence”.

Pause here for a moment!

Let’s snip the scissor on our thought and try to understand what’s the matter in reality.

[A and B are scissors congruent if A can be cut into finitely pieces–each of which is homeomorphic to a disc and bounded by a curve of finite length–which can be rearranged to form B (ignoring boundaries).]

We are essentially cutting out congruent pieces of a polygon from both the polygons each time, right! Will this process ever end?  Is so, why? Or how?
Also, observe if a polygon P can be transformed into Q in this way denote it by P  Q to avoid too much clumsiness. Now,

P \(\rightarrow\) Q means Q \(\rightarrow\) P 

P \(\rightarrow\) Q, Q \(\rightarrow\) R means P \(\rightarrow\) R

P  \(\rightarrow\) P

It is easy right

Now, this sort of relationship is called Equivalence Relation which has a nice use that is if we can show that any polygon can be transformed a certain central figure of the same area then the theorem will be proved true. We want that central figure to have a minimum of parameters(why) defining it. Hence an easy choice is any regular polygon with one parameter as it is always fixed with a given area.

Ok, before proceeding let’s adventure through the basic and simple examples of dissection of basic figures: Triangles, Squares, and Rectangles to get some ideas and maybe it can be reduced to these cases only.

Triangle \(\rightarrow\) A Rectangle (Not Predefined):

Rectangle  \(\rightarrow\) A Square (Always fixed given an area):

2 Rectangles \(\rightarrow\) A Square (Always fixed given an area):

Ok, due to the rigidity of the square structure, let us consider converting the two rectangles to their corresponding squares by the method described above.

Now observe that we need to change the sum of two square areas to a single square area, Hey that is exactly the Pythagoras Theorem type. Do we know a dissection proof Pythagoras Theorem? Let’s do it in a separate way.

2 Squares \(\rightarrow\) A Square (Always fixed given an area):

n rectangles \(\rightarrow\) A Square (Always fixed given an area):

Simple like induction, add a rectangle every time after transforming two rectangles into a square and follow the previous steps.

                        Have you seen the usefulness of the Rigidity of the Square Structure and also the hunch of the theorem?

Steps of Proof:

    1. Triangulate the Polygon

    1. Each Triangle -> Rectangle

  1. Set of Rectangles -> A Square by the method described above.

Now given that every polygon with the same area can be transformed into a square with the same area as those of the triangle. QED!

Yaaayy, we proved it! It wasn’t too difficult right!

Now, let’s return to the original question!

Why four?

Let's keep this suspense till the next post, while in the meantime you get hands-on experience with scissors and paper and enjoy your own journey and tickle your brain to think about "Why Four?" and also "How Four?".

Please share your experiences and thoughts in the comments, which will make the math become alive with Paper, Scissors and You.

AM GM Inequality, Euler Number - Stories in Real Analysis

A.M.- G.M. Inequality can be used to prove the existence of Euler Number. In this discussion, we venture into the fascinating journey from classical inequalities to modern real analysis.

Watch the video

A short code in Python to check boundedness of a classical sequence converging to Euler Number.

Lets invent Euler Number!

Why should we study real analysis? Once you are inside the first course of this beautiful subject, you will find hundreds of complicated theorems and formulas floating around the canvas. What makes this journey into rigorous deductive reasoning interesting is: we get to invent new numbers and functions!

Euler Number is one example. But it is just one of the infinitely many other numbers that were invented using tools of real analysis.

The story begins with an age old formula from bankers:

Stories in Real Analysis - Inverse Maps

Inverse maps are very important in Real analysis. They form the backbone of the definition of continuity.

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Proper Metric Spaces can be modeled by Rays!

‘Proper’ is a heavily overloaded term, both in life and in mathematics. It may mean different stuff in different contexts. Thankfully mathematics is far less complicated that life and we can rigorously define properness.

Proper Function: A continuous function ( f : X  \to Y ) between topological spaces is proper if the preimage of each compact subset of Y is compact.

Proper Space: A metric space (X, d) is proper if every closed ball ( B[x, r] = { y \in X | d(x, y) \leq r } ) in X is compact.

Intuition: Proper Spaces have much in common with Euclidean Spaces. Why? Heine Borel Theorem ensures that, closed balls are compact in Euclidean Space. Proper Spaces own this property as well.

There is a fancy description of Proper Spaces using Proper Functions.

Also see

College Mathematics Program at Cheenta

Theorem:

Let (x_0 ) be an element of the metric space (X, d) and define ( d_0 : X \to [0,\infty) by d_0 (x) = d(x_0, x) ) . Then (X, d) is proper iff ( d_0 ) is proper.

We will rigorously prove this theorem. But first, lets draw some pictures.

Driving Idea: Which points in X map to the point ( a \in [0, \infty) ) ? They must be the points which are a distance away from ( x_0 ). Hence they are circles in X centered at ( x_0 ).

Proper Metric Spaces

Proof:

(It is useful to try and write your own proofs first)

—> Suppose \( d_0 \) is a proper map. Then we will show that (X, d) is a proper space (that is all closed balls are compact).

Consider the compact set [0, a] (a is a finite number) in ( [0, \infty) ). This is a compact set (in ( [0, \infty) ).

Then its inverse image ( d_0^{-1} ([0, a) ) ) is compact in (X, d). (This is true because we have assumed (d_0 ) is a proper map, hence inverse images of compact sets will be compact).

But ( d_0^{-1} ([0, a) ) ) is a closed ball centered at ( x_0 ) of radius a. Thus we showed that all closed balls centered at ( x_0 ) are compact.

Next we will show that any other closed ball is compact. Consider a closed ball of radius r, centered at ( x_1 \in X ). Suppose ( d(x_0, x_1) = t ) We take the ball B’ of radius t + r centered at ( x_0 ).

If ( x \in B ) then ( d (x , x_1 ) \leq r ). Next we use the triangle inequality to compute:

$$ d(x_0, x) \leq d(x_0, x_1) + d(x_1, x) \leq t + r $$

Hence x is in ball B’ centered at ( x_0 ) . Therefore B is contained in B’.

Suppose ( { U_{\alpha} }{\alpha \in \Lambda}  ) is an arbitrary open cover of B (the closed ball centered at (x_1) ). Since B is closed (by assumption), therefore ( B^c ) is open and ( { { U{\alpha} }_{\alpha \in \Lambda} , B^c } ) is an open cover for B’.

Earlier we showed that all closed balls centered at ( x_0 ) are compact. B’ is one such balls. Hence it is compact. Hence ( { { U_{\alpha} }_{\alpha \in \Lambda} , B^c } ) has a finite subcover that covers B’. Since B is inside B’, this finite subcover also works for B’. If necessary, by removing ( B^c ), we get a finite subcover of B. Hence B is compact.

<—- Suppose (X, d) is proper. Then we will show that \( d_0 \) is a proper map.

Suppose V is a compact subset of ( [0, \infty ) ). By Heine Borel Theorem, it is closed (contains all its limit points) and bounded. Since V is bounded, it has an upper bound. By Completeness axiom, it has a least upper bound L. Since it is closed, this least upper bound is inside V.

( d_0^{-1} (L)  ) is the circle of radius L centered at ( x_0 ). Every other point in the pre-image of V, is on a circle centered at ( x_0 ) of radius less than or equal to L. Hence the pre-image of V is bounded.

We will show that every infinite sequence has a convergent subsequence (sequential definition of compactness).

Suppose ( < x_n > ) is an infinite sequence in the pre image of V. Then ( d_0 ( x_n ) = a_n ) is an infinite sequence in V. As V is compact, it has a convergent subsequence (d_0 (x_{n_k}) = a_{n_k}) that converges to some ( a \in  V ).

The preimage of a under (d_0 ), is the collection of all points from ( x_0 ) at a distance away. The distance from ( < x_{n_k} > ) to (B [x_0 , a] ) is 0 as ( d (x_{n_k} , x_0) ) approaches a as (n_k \to \infty ). If no point on ( \partial B ) (boundary of the ball) is a limit point of the sequence, then we can build an open cover using open balls ( U_x ) at each point on the boundary that completely misses the sequence (< x_{n_k} > ) and int (B).

Since ( B[x, a]) is compact, this open cover has a finite subcover. Let ( int (B), U_{x_1} ,  ... , U_{x_n} ) be the finite subcover. This leads to a contradiction as ( < x_{n_k} > ) cannot be closer to the boundary than the minimum of the distances of these finitely many open sets (each of which completely misses the sequence).

Proper Spaces share more properties with Euclidean Spaces. For example, every proper metric space is complete.

Proof:

Suppose ( < x_k> ) is Cauchy in a proper space (X, d). That is ( \forall \epsilon > 0 \exists N \in \mathbb{N} \ni \forall m, n > N, d(x_m, x_n) < \epsilon ).

This implies ( | d(x_m, x_0) - d(x_n, x_0) | < d(x_m, x_n) < \epsilon ). Therefore ( |d_0 (x_m) - d_0 (x_n) | < \epsilon ) in ( [0, \infty) ).

Hence ( d_0 ( x_k) ) is Cauchy in ( [0, \infty) ). Since ( [0, \infty) ) is complete, therefore this converges to some nonnegative number a.

Finally using arguments similar to the last part of the previous proof, we are done.

One Point Compactification

Theorem: Show that a continuous function ( f : X \to Y ) between proper metric spaces is proper iff the obvious extension ( f : X^* \to Y^* )  between one-point compactification spaces is continuous.

Proof: 

Recall One Point Compactification:

Put ( {\displaystyle X^{}=X\cup {\infty }}  ) , and topologize ( {\displaystyle X^{}}  ) by taking as open sets all the open subsets U of X together with all sets ( {\displaystyle V=(X\setminus C)\cup {\infty }}  ) where C is closed and compact in X. Here, ( {\displaystyle X\setminus C} )  denotes setminus. Note that ( {\displaystyle V} ) is an open neighborhood of ( {\displaystyle {\infty }} ), and thus, any open cover of ( {\displaystyle {\infty }} ) will contain all except a compact subset ( {\displaystyle C} ) of ( {\displaystyle X^{}} ) , implying that ( {\displaystyle X^{}} ) is compact.

Proper --> Continuous

Suppose ( f : X \to Y ) is proper. We will show that ( f^* ), the extension of f, is continuous. Toward that extent, we will show that inverse of any open set is open. Clearly if, ( V \subset X^* ) does not contain ( \infty_Y ) then its preimage is open in ( X^* ) as f is continuous.

If ( V \subset Y^* ) contains ( \infty_Y ) then( {\displaystyle V=(Y \setminus C)\cup {\infty_Y }}  ) where C is closed and compact in X. Then

( \displaystyle { {f^}^{-1} ( (Y \setminus C) \cup { \infty_Y } ) \ = { {f^}^{-1}  (Y) -  {f^}^{-1} ( C) } \cup {f^}^{-1} { \infty_Y } \ = {X - f^{-1}(C)}  \cup {\infty_X } } )

SInce f is proper ( f^{-1} (C) ) is compact and we have an open set of ( X^* ) as the preimage.

Continuous --> Proper

Suppose V is an open set in ( Y^* ) containing ( \infty_Y ). Since ( f^* ) is continuous, its preimage must be open.

( {\displaystyle V=(Y \setminus C)\cup {\infty_Y }}  ) where C is closed and compact in X

( \displaystyle { {f^}^{-1} ( (Y \setminus C) \cup { \infty_Y } ) \ = { {f^}^{-1}  (Y) -  {f^}^{-1} ( C) } \cup {f^}^{-1} { \infty_Y } \ = {X - f^{-1}(C)}  \cup {\infty_X } } )

This implies ( {X - f^{-1}(C)}  \cup {\infty_X }  ) is open in ( X^* ) containing ( \infty ). This implies ( f^{-1} (C) ) is compact (due to the characterization of open sets containing infinity). SInce for all compact subsets C of Y, the above argument is valid, therefore f is proper.

Compact Set, Proper Spaces and Annulus

Euclidean Spaces have a very nice property. In ( \mathbb{R}^n ) (equipped with standard Euclidean metric), every closed and bounded set is a compact set. The converse is also true. Every compact set is closed and bounded). This property is known as Heine Borel Theorem.

(more…)

Why is it interesting to laminate a genus-2 surface?

 The term double torus is occasionally used to denote a genus 2 surface.

We are interested to understand the structure of a group G (is it abelian, or a product of abelian groups, or a free group of some kind etc.). In general, this is a (very) hard question. One strategy is to let G act on some well known (topological) space. Often by studying the effect of this group action on the space, it is possible to comment on the algebraic structure of the group.

Why this ‘indirect’ method should be regarded as ‘natural’? It is useful to think about elements of groups as ‘actors’. Their true color is revealed, only when they are unleashed in a stage (topological space).

However, we have to find the right space on which the group under scanner is to be let loose. A lot of effort goes into the construction (and investigation) of topological spaces which will be effective stages for group action.

Bass Serre theory produced a wonderful (topological) space that produces important information about groups acting on them. They are cleverly designed simplicial trees. We look at the stabilizers of vertices and edges of this tree. This process reveals a lot of information about the structure of the group.

Simplicial trees are different from (non-simplicial) R-trees (real trees). The key distinction is: R-trees have non-discrete branching points. There is an interesting construction in this context. Let G act isometrically (preserving distances), on a sequence of negatively curved spaces. Then we have a natural isometric action of G on an R-tree in the Gromov Hausdorff limit. 

This makes R-trees the final destination space of the isometric Group action. It makes sense to study the R-trees. After all, they are stage of action of the Group that is our ultimate object of interest. A process of resolution leads us from R-trees to (measured) laminated 2-complexes.

(Ref: Bestvina; 1999)

(Image: https://conan777.wordpress.com/tag/measured-lamination/)