그림입니다.
원본 그림의 이름: Bigbook-Linearalgebra-cover.jpg
원본 그림의 크기: 가로 587pixel, 세로 798pixel         그림입니다.
원본 그림의 이름: sglee-2.gif
원본 그림의 크기: 가로 319pixel, 세로 431pixel

 



Chapter 6

 

 


Linear Transformations

 

6.1 Matrix as a Function (Transformation)

6.2 Geometric Meaning of Linear Transformations

6.3 Kernel and Range

6.4 Composition of Linear Transformations and Invertibility

6.5*Computer Graphics with Sage

Exercises


So far, we have considered matrix mainly as a coefficient matrix from systems of linear equations. Now, we consider a matrix as a function.


We have observed that the set of vectors together with two operations reborn as an algebraic structure, namely a subspace(vector space).

Matrix will be reborn as a linear transformation, which is a function that preserves the operations in a vector space.

And linear transformations are used for noise filtering in signal processing and analysis in engineering processes.

We show a linear transformation from -dimensional space to -dimensional space can be expressed as a matrix .

We shall also look at geometric meaning of linear transformations from to and applications in computer graphics.




6.1 Matrix as a Function (Transformation)

 

 Reference video: https://youtu.be/KHTR69HDSNs http://youtu.be/YF6-ENHfI6E

 Practice site: http://matrix.skku.ac.kr/knou-knowls/cla-week-8-Sec-6-1.html

Matrix can be considered as a special function with the property of linearity.

Such a function play an important role in science and various areas in daily life,

such as mathematics, physics, engineering control theory, image processing, sound signal, and computer graphics.

 

What is a Transformation?

 

Definition

 [Transformation]

 

 

 

 

A function, whose input and output are both vectors, is called a transformation. For a transformation , is called an image of by , and is called a pre-image of .

       그림입니다.

 

As a special case of transformations, , for some matrix and a vector is called a matrix transformation.

 

 

 

 

   

          



[Remark]

Computer simulation

 

 

 

 

 [Matrix transformation)

 http://www.geogebratube.org/student/b73259#material/22419

 

              

 

 

 

 

 


  

Definition

[Linear Transformation]

 

 

 

 

If a transformation from to , satisfies the following two conditions

for any vectors and for any scalar ,

 

    (1)                (2)    

 

then is called a linear transformation from to .  Especially,

a linear transformation from to itself,

                   is called a linear operator on .

 

 

 

 



Show that is a linear transformation if we define , for any vector in , as follows     


For any two vectors , in and for any scalar ,

 (1)

             

            .


 (2) .

Therefore, by definition, is a linear transformation from to .      




Let, . Show that T is a linear transformation. 


For any two vectors and in and for any scalar ,

 

  (1)

                 

               .

  

  (2)

           

 Therefore, T is a linear transformation.         


 This type of linear transformation is called orthogonal projection on -plane.



If we define as follows, show that is not a linear transformation.

.


For any two vectors, , in ,

     .


However,

                  . Hence .


Therefore, we conclude that is not a linear transformation.  



[Remark]

Special Linear Transformations

 

 

 

 

 zero transformation: For any , if we define as ,

      then is a linear transformation. This is called a zero transformation.

 

 identity transformation: For any , if we define as ,

     then is a linear transformation. This is called an identity transformation

 

 matrix transformation: For any matrix and for any vector in ,

      if we define ,

     then is a linear transformation from to . This is called a matrix transformation.

 

 

 

 

 



 

Let is defined as follows. Show is a linear transformation.


For any two vectors in and for any scalar ,


 (1)

             


 (2)
     
and hence, is a linear transformation.                                 



A linear transformation from is

 ,

  is a matrix transformation for a matrix .      



   

Theorem 6.1.1 [Properties of Linear Transformation]

 

If is a linear transformation, then it satisfies the following conditions:

 

 (1) .

 (2) .

 (3) .


(1) Since , .

      (2)  

      (3)              

 

    Each linear transformation  from to can be expressed as a matrix transformation.



 Let be any linear transformation. For elementary unit vectors,

       of and for any , we have

 

                                      

 and   as , , , are matrix, we can write them as

 

                         .

 

Therefore any linear transformation can be expressed as


  

 

            

 

                     (1)


Now let be an matrix which has , , , as it's columns.

              

Then, 

                    .


The above matrix is called the standard matrix of and is denoted by .

Hence, the standard matrix of the linear transformation given by (1) can be found easily from

     the column vectors by substituting the elementary unit vectors to in that order.



   

Theorem  6.1.2 [Standard Matrix]

 

 If is a linear transformation,

    then the standard matrix  of  T  has the following relation for .    

 

                                      

 

where .



For a linear transformation , ,

                    by using the standard matrix of , rewrite it as .


 Let , which columns are , then

          as .

  

http://matrix.skku.ac.kr/RPG_English/6-MA-standard-matrix.html 

                                                                      

x, y, z = var('x y z')

h(x, y, z) = [x+2*y, -x-y, z, x+z]

T = linear_transformation(QQ^3, QQ^4, h)  # define LT

C = T.matrix(side='right')                 # standard matrix

x0 = vector(QQ, [2, -3, 3])

print C

print T.domain()                           # domain

print T.codomain()                         # codomain

print T(x0)                                 # image

print C*x0            # product of standard matrix and a vector

                                                                   

[ 1  2  0]

[-1 -1  0]

[ 0  0  1]

[ 1  0  1]

Vector space of dimension 3 over Rational Field

Vector space of dimension 4 over Rational Field

(-4, 1, 3, 5)

(-4, 1, 3, 5)                                                                     ■




6.2 Geometric Meaning of Linear Transformations

 

 Reference video: https://youtu.be/hJXsKubtgms https://youtu.be/cgySDj-OVlM

 Practice site: http://matrix.skku.ac.kr/knou-knowls/cla-week-8-Sec-6-2.html

사각형입니다.

In this section, we study the geometric meaning of linear transformations.

For a given image, continuous showing of series of images with small variations makes a motion picture.

Linear transformation can be applied to computer graphics and numerical algorithms, and it is an essential tool for many areas such as animation.

 

Linear Transformation from to  

 


        


  A linear transformation defined by moves a vector

       to an another vector .

 


  [rotation, symmetry, orthogonal projection]

   We illustrate a few linear transformations on .


  (1) is a linear transformation which rotates a vector in counterclockwise by around the origin. 

                                                                 


  (2) An orthogonal projection on -axis is a linear

     transformation.

                      

                                        

  (3) A symmetric movement around -axis is a linear transformation.

                                                         


●   http://matrix.skku.ac.kr/sglee/LT/11.swf 


          

 


Find the standard matrix for a linear transformation which moves a point in

    to a symmetric image around the given line.


                                  (1) -axis                      (2) line


Symmetric (linear) transformation around -axis and the line are given in the following figures.

 

                      ,   

                      ,     .                                                


●   http://matrix.skku.ac.kr/sglee/LT/22.swf 

●   http://matrix.skku.ac.kr/sglee/LT/44.swf 

 


       



 [Remark]  Simulation

 

 

 

 

 

[linear transformation]

            http://www.geogebratube.org/student/m9703

[symmetric transformations and orthogonal projection transformations]           

          http://www.geogebratube.org/student/m9910

[rotation]

그림입니다.         

        http://www.geogebratube.org/student/m9702  

 

 

 

 



Linear transformation which moves any vector in to a symmetric image around a line,

  which passes through the origin with angle between the -axis and the line,

   can be expressed by the following matrix presentation .

 

           .                                        



In , if , , i.e. .




As shown in the picture, let us define an orthogonal projection as a linear transformation (linear operator)

            which maps any vector in to the orthogonal projection on a line,

    which passes through the origin with angle between the -axis and the line.

 

  Let us denote the standard matrix correspond to is .

            (the same direction with a half length)

  

     

          

 


           


 In , if , is a projection onto the -axis.




 [Remark]

 shear transformations (computer simulation)

 

 

 

 

(1) : shear transformation along the -axis with scale

(2) : shear transformation along the -axis with scale

 

http://www.geogebratube.org/student/m9912 

 

            

 

 

 

 

 

   

Definition   [Isometry]

 

 

 

 

 

A linear transformation , which preserve the magnitude

 (or length of a vector), , is called Euclidean isometry.

 

 

 

 

  

 

Theorem  6.2.1

 

For a linear operator , the following statements are equivalent:

 

 (1) ,            [isometry].

 (2) ,   [preserve the dot (inner) product].


 

Definition  [Orthogonal matrix]

 

 

 

 

 

For a square matrix , if then is called an orthogonal matrix.

 

 

 

 


 

For any real number , is an orthogonal matrix, and  .              



Verify the following matrices are orthogonal matrix.

   ,


     Verify , by using the Sage.                 

                     

http://matrix.skku.ac.kr/RPG_English/6-TF-orthogonal-matrix.html 

 

                                                                     

A=matrix(QQ, 2, 2, [3/5, -4/5, 4/5, 3/5])

B=matrix(3, 3, [1/sqrt(3), 1/sqrt(2), 1/sqrt(6),

               1/sqrt(3), -1/sqrt(2), 1/sqrt(6),

               1/sqrt(3), 0, -2/sqrt(6)])

print A.transpose()*A              # confirm the orthogonal matrix

print

print B.transpose()*B

                                                                  

[1 0]       [1 0 0]

[0 1]       [0 1 0]

           [0 0 1]                                                                                                    ■


   

Theorem 6.2.2

 

For any matrix , the following statements hold:

 

 (1) The transpose of an orthogonal matrix is an orthogonal matrix.

 (2) The inverse of an orthogonal matrix is an orthogonal matrix.

 (3) The product of orthogonal matrices is an orthogonal matrix.

 (4) If    is an orthogonal matrix, then or .


(1) and (2) are left as an exercise to the reader.

      (3) If and , then

and hence is an orthogonal matrix.

      (4) Observe that

              or .                                                                                


          

Theorem  6.2.3

 

For any matrix , the following statements are equivalent:

 

 (1) is an orthogonal matrix.

 (2) , .

 (3) , .

 (4) The row vectors of are orthonormal.

 (5) The column vectors of are orthonormal.


(1) (2): 

                         


      (2) (3): 

                 

                               

      and

                  .

      Hence .

      (3) (1):  ,

                   (Kronecker's delta) 


      We skip the detailed proof of (4) and (5) as we can get the result easily

         from the definition of the orthogonal matrix, , and (1).                  





6.3 Kernel and Range

 

 Reference video: https://youtu.be/vMulGT9RiI0 http://youtu.be/9YciT9Bb2B0

 Practice site: http://matrix.skku.ac.kr/knou-knowls/cla-week-8-Sec-6-3.html 

사각형입니다.

We will show that the subset of a domain , which maps to zero vector by a linear transformation,

becomes a subspace. We will also show the set of images under any linear transformation forms a subspace in the co-domain.

Finally, we introduce the concept of isomorphism.

 


 Definition [Kernel]

 

 

 

 

 

Let be a linear transformation. The set of all vectors in ,

whose image becomes by , is called kernel of and is denoted by .

 

   That is,      .

 

 

 

 

  

                                   




      Find the for a linear transformation where .


          .     

 

 

Find the for a linear transformation , where .

     For any ,

             

               ,


             and hence, .                                            


 

  

 Definition  [one-to-one]

 

 

 

 

 

For a transformation , if ,

              then it is called one-to-one (injective).

 

 

 

 



   

 Definition  [onto]

 

 

 

 

 

For a transformation , if there exist for any given ,

  such that ,  then it is called onto (surjective).

 

 

 

 


   

Theorem  6.3.1

 

Let and are vector spaces and is a linear transformation.

     Then is one-to-one       if and only if    .


 As , and is one-to-one,

              


         

              

        is one-to-one.                                                                  



Let us define a linear transformation as . Is one-to-one?

   As ,

    the only element in this set is .

    Hence , and is one-to-one.                             




Is a linear transformation one-to-one if it is defined as

?


 Since ,

 the system of linear equations has infinitely many solutions.

 Hence,  and by theorem 6.3.1, is not one-to-one.                          


 

① Verify whether a linear transformation is one-to-one or not

                                                                     

U = QQ^3                              # vector space

x, y, z = var('x, y, z')

h(x, y, z) = [x+2*y-z, y+z, x+y-2*z]

T = linear_transformation(U, U, h)  # generate a linear transformation

print T.is_injective()                    # check the one-to-one

                                                                   

False


② Find the Kernel of the linear transformation

                                                                     

T.kernel()                    # verify by finding kernel

                                                                   

Vector space of degree 3 and dimension 1 over Rational Field

Basis matrix:

[   1 –1/3  1/3]             # kernel = span( [1, -1/3, 1/3] ).         



 Let be an matrix. If we define a linear transformation as ,

      then is a solution space of the system of linear equations .



   

Theorem 6.3.2

 

Let are vector spaces and is a linear transformation.

 Then is a subspace of .  Hence the subspace is called a kernel.

 

    

             

            

                 is a subspace of .                                  


 

   Find the kernel of when with .


 

                                                                     

A = matrix(2, 2, [1, 1, 1, -1])

print A.right_kernel()                          # kernel of A

                                                                    

Free module of degree 2 and rank 0 over Integer Ring

Echelon basis matrix:

[]                   # kernel has only a Zero vector                            


  

Definition [Isomorphism]

 

 

 

 

 

For a linear transformation , the set of all for ,

is called range of and is denoted by .  That is,

 

.

 

Especially, if then is called surjective or onto.

 

If a linear transformation is one-to-one and onto, then and is called an isomorphism from to .   

 

 

 

 

 

 

  Find the range of the linear transformation .


 

    

           . Note that, is not surjective.                               


  is not an isomorphism as it is not surjective.




Let and

 . It is easy to see that both and are subspaces of .

   If we define as the following linear transformation,


                       


then is both one-to-one and onto, and hence it is an isomorphism.                                



   

Theorem  6.3.3

 

For a linear transformation ,   is a subspace of  .


 , such that ()  

        

               


       ,   

                

       is a subspace of .                                                                                      




Let be an matrix, if we define a linear transformation as ,

     then is a column space of .


    Let , that is, be an matrix 's th column vector.

    Then for any vector ,

            .

   That is, any image can be expressed as a linear combination of column vectors of .

                                                              




Theorem  6.3.4

 

For a linear transformation defined by a matrix   

satisfies the following two properties. 

 (1) is one-to-one. column vectors  of are linearly independent.

 (2) is onto. row vectors of are linearly independent.


(1) is one-to-one

            There is a unique which satisfies .

             column vectors of are linearly independent.


      (2) is onto

            For 's column vectors ,

               

            In RREF, the number of leading ones is .

            row rank of is .

             row vectors of are linearly independent.                                                       




Verify the following by using the Sage.


(1) Let . is one-to-one but not onto.


 http://sage.skku.edu


① define a linear transformation

                                                                    

U = QQ^2

V = QQ^3

A = matrix(QQ, [[1, 0], [0, 1], [0, 0]])

T = linear_transformation(U, V, A, side='right'

# linear transformation

print T

                                                                  

Vector space morphism represented by the matrix:

[1 0 0]

[0 1 0]

Domain: Vector space of dimension 2 over Rational Field

Codomain: Vector space of dimension 3 over Rational Field


② check the surjectivity (onto)

                                                                    

print T.image()                # generate the range

print T.is_surjective()          # check the surjectivity (onto)

                                                                  

Vector space of degree 3 and dimension 2 over Rational Field

Basis matrix:

[1 0 0]

[0 1 0]

False 


③ check the injectivity (one-to-one)

                                                                    

print T.kernel()                  # generate the kernel

print T.is_injective()             # check the injectivity (one-to-one)

                                                                  

Vector space of degree 2 and dimension 0 over Rational Field

Basis matrix:

[]

True


(2) Let . is onto but not one-to-one.


 http://sage.skku.edu


① define a linear transformation

                                                                    

U = QQ^3

V = QQ^2

A = matrix(QQ, [[1, 0, 0], [0, 1, 0]])

T = linear_transformation(U, V, A, side='right')  

# linear transformation

print T

                                                                  

Vector space morphism represented by the matrix:

[1 0]

[0 1]

[0 0]

Domain: Vector space of dimension 3 over Rational Field

Codomain: Vector space of dimension 2 over Rational Field


② check the surjectivity (onto)

                                                                    

print T.image()                # generate the range

print T.is_surjective()          # check the surjectivity (onto)

                                                                  

Vector space of degree 2 and dimension 2 over Rational Field

Basis matrix:

[1 0]

[0 1]

True


③ check the injectivity (one-to-one)

                                                                    

print T.kernel()                  # generate the kernel

print T.is_injective()             # check the injectivity (one-to-one)

                                                                  

Vector space of degree 3 and dimension 1 over Rational Field

Basis matrix:

[0 0 1]

False                                                                                      ■


   

Theorem 6.3.5

 

Let be an matrix. If is a linear transformation,

     is one-to-one    if and only if     is onto.


  is one-to-one

                   There is a unique which satisfies .

                   In 's RREF, number of leading ones is .

                   For 's column vectors ,

                     

  

                    is onto                                



Invertible Matrix theorem

   

Theorem 6.3.6 [Invertible Matrix Theorem]

 

Let be an matrix, the following statements are all equivalent.

 

 (1) column vectors of are linearly independent.

 (2) row vectors of are linearly independent.

 (3) has only trivial solution .

 (4) For any vector , has a unique solution.

 (5) and are column equivalent.

 (6) is invertible.

 (7)

 (8) is not an eigenvalue of .

 (9) is one-to-one.

 (10) is onto.




6.4 Composition of Linear Transformations and Invertibility

 

 Reference video: https://youtu.be/AfjRc-IxZQk http://youtu.be/EOlq4LouGao

 Practice site: http://matrix.skku.ac.kr/knou-knowls/cla-week-8-Sec-6-4.html

In this section, we study the composition of  two or more linear transformations as continuous product of matrices.

We also study the geometric properties of linear transformation by connecting inverse functions and inverse matrices.


  

Theorem 6.4.1 [Composition of Functions]

 

If both and are linear transformations, then the composition

 

              is also a linear transformation.

 

 


 

Theorem 6.4.2

 

For linear transformations and ,

 (1)   is one-to-one implies is one-to-one.

 (2)   is onto implies is onto.

 

 (1) If , for all , then .

                          ( is one-to-one)

                      ∴ is one-to-one.


           (2) If is onto, then for , there exist such that .

               That is, there exist which satisfy .

                Since ,  there exist such that

                                ∴ is onto.                                                                               


For the case of composition of two linear transformations,

  the corresponding standard matrix is the product of two standard matrices from each linear transformation.



 That is, let , and

     has a standard matrix , has standard matrix .

   Then the linear transformation has the standard matrix

                                         

                                              .



                그림입니다.
원본 그림의 이름: 6-4-Comp.jpg
원본 그림의 크기: 가로 593pixel, 세로 400pixel       



Let the standard matrix of a linear transformation  be .

     If an inverse transformation exist, then the standard matrix of is the inverse of the matrix .



    Let are linear transformations which rotate and (counterclockwise)

   respectively around the origin. The corresponding standard matrices are as follows.

                      

                     ,


  As the composition of these two transformations rotates around the origin,

    the standard matrix of   is as follows.

                       .

Also the product of standard matrices of and are as follows.


         

                   

                   .                              



   As shown in the picture, find a matrix transformation which transform a circle with radius 1 to the given ellipse.


             


First we find a transformation which expands 3 times around the -axis, and expands 2 times around the -axis.

   Then take a transformation which rotates around the origin.

  The first transformation is , and

   hence the standard matrices for and the rotation transformation are

                   .

   Therefore, the standard matrix for the composition is the product of two standard matrices.


             .                                          



 [Remark] Computer simulation

 

 

 

 

 

 [Matrix Transformation] http://www.geogebratube.org/student/m57556

 

그림입니다.

 

 

 

 

 



 Similarly a composition of three or more linear transformations,

    the standard matrix of the composition is the product of each standard matrix in that operation order.


   

Theorem 6.4.3

 

A function is invertible if and only if is one-to-one and onto.


   

Theorem 6.4.4

 

If a linear transformation is invertible,

then  is also a linear transformation.


 Inverse transformation of composition of transformation:

                    

                    


 [Remark] Computer simulation

 

 

 

 

 

[ Scaling (expanding and compressing) transformation]

      http://www.geogebratube.org/student/m11366

 

            그림입니다.

 

 

 

 

 




6.5 *Computer Graphics with Sage

 

 Reference video: http://youtu.be/VV5zzeYipZs

 Practice site: http://matrix.skku.ac.kr/Lab-Book/Sage-Lab-Manual-2.htm

                        http://matrix.skku.ac.kr/Big-LA/LA-Big-Book-CG.htm  

    

Computer graphics plays a key role in automotive design, flight simulation, and game industry.

For example, a 3 dimensional object,  such as automobile, its data (coordinates of points) can be described as a matrix.

If we transform the location of these points, we can redraw the transformed object from the points which are newly generated.

If this transformation is linear, we can easily obtain the transformed data by matrix multiplication.

In this section, we review several geometric transformations which are used in computer graphics.


Geometric meaning of Linear Transformation 1

 

(Linear Transformation of Polygon’s Image)


By using the Sage, draw a triangle with three vertices , , and ,

    a triangle expanded twice, a figure by a shear transformation along the -axis with scale 1, and a triangle which is rotated counterclockwise by .



 First of all, in order to define the above linear transformations, we input the following linear transformations by using matrix.

def matrix_transformation(A, L):

    n=matrix(L).nrows()    # list L’s number of elements

    L2=[[0,0] for i in range(n)]   # define a new list L2

    for i in range(n):

        L2[i]=list(A*vector(L[i]))  # L2=A*L

    return L2   # return L2

print "The matrix_transformation function is activated"#confirm whether it is applied                     



 Then, we define appropriate standard matrices to fit the problems’ condition.

A=matrix([[2,0], [0,2]])   # Expanding twice of given image

B=matrix([[1,1], [0,1]])   # shear transformation along the -axis with scale 1

C=matrix([[cos(pi/3), -sin(pi/3)], [sin(pi/3), cos(pi/3)]])

                          # rotate counterclockwise the given image by



Draw a triangle which has three vertices , , by using ploygon.

L1=list( [ [0,0], [0,3], [3,0] ])   # input three vertices

SL1=polygon(L1, alpha=0.3, rgbcolor=(1,0,0))   # draw a polygon which passes through the given three points

SL1.show(aspect_ratio=1, figsize=3)

                         



Draw a twice expanded triangle from the given triangle. 

L2=matrix_transformation(A, L1)  # find new three points by a linear transformation

SL2=polygon(L2, alpha=0.8, rgbcolor=(0,0,1)) # draw a polygon which passes through the given three points

SL2.show(aspect_ratio=1, figsize=3)

                          



Draw a shear transformed figure along the -axis with scale 1 from the given triangle.

L3=matrix_transformation(B, L1)  # find new three points by a linear transformation

SL3=polygon(L3, alpha=0.8, rgbcolor=(1,0,1)) # draw a figure which passes 

through the given three points

SL3.show(aspect_ratio=1, figsize=3)

                        



Draw a figure which is rotated counterclockwise by from the given triangle.

L4=matrix_transformation(C, L1) # find new three points by a linear transformation

SL4=polygon(L4, alpha=0.4, rgbcolor=(0,0,1)) # draw a figure which passes

through the given three points

SL4.show(aspect_ratio=1, figsize=3)

                     



Show the above four figures in the same frame.

(SL1+SL2+SL3+SL4).show(aspect_ratio=1, figsize=3)

                




Geometric meaning of Linear Transformation 2

(Linear Transformation of Line’s Image) 


 Draw the alphabet letter on the plane. Then draw figures which expands the original figure twice,

    sheer transforms along the -axis with scale 1, and rotates counterclockwise by  .


 First of all, in order to define the above linear transformations, we input the following linear transformations by using matrix. 

def matrix_transformation(A, L):

    n=matrix(L).nrows()    # list L’s number of elements

    L2=[[0,0] for i in range(n)]    # define a new list L2

    for i in range(n):

        L2[i]=list(A*vector(L[i]))   # L2=A*L

    return L2    # return L2

print "The matrix_transformation function is activated"#confirm whether it is applied



 Then, we define appropriate standard matrices to fit the problems’ condition.

A=matrix([[2,0], [0,2]])   # Expanding twice of given image

B=matrix([[1,1], [0,1]])   # shear transformation along the -axis with scale 1

C=matrix([[cos(pi/3), -sin(pi/3)], [sin(pi/3), cos(pi/3)]])

                          # rotate counterclockwise the given image by



Draw an alphabet letter S by using the line function.

L1=list( [ [0,0], [4,4], [-3,12], [0,15], [3,12], [4,12], [0,16], [-4,12], [3,4], [0,1], [-3,4], [-4,4], [0,0] ]) # input the data which compose letter

SL1=line(L1, color="red") # draw a figure which passes through the given points  SL1.show(aspect_ratio=1, figsize=5)

                               


Draw a twice expanded letter S from the given figure. 

L2=matrix_transformation(A, L1) # compute new points’ coordinates by a linear transformation

SL2=line(L2, color="purple") #draw a figure which passes through the given points

SL2.show(aspect_ratio=1, figsize=5)

                            



Draw a sheer transformed figure along the -axis with scale 1 from the given S.

L3=matrix_transformation(B, L1)   # compute new points’ coordinates by a linear transformation

SL3=line(L3, color="blue")  # draw a figure which passes through the given points

SL3.show(aspect_ratio=1, figsize=5)

                         



Draw a figure which is rotated counterclockwise by from the given letter S.

L4=matrix_transformation(C, L1)   # compute new points’ coordinates by a linear transformation

SL4=line(L4, color="green")  #draw a figure which passes through the given points

SL4.show(aspect_ratio=1, figsize=5)

           

                    



Show the above four figures in the same frame.

(SL1+SL2+SL3+SL4).show(aspect_ratio=1, figsize=5)