Undergrad Linear Isomorphisms: Understand & Apply

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SUMMARY

This discussion focuses on understanding and applying linear isomorphisms, particularly through the example of a rotation transformation in the 2D plane. Participants discuss how to demonstrate that the mapping of vectors (1,0) to (1/2, √3/2) and (0,1) to (-√3/2, 1/2) constitutes a linear isomorphism. Key concepts include linearity, injectivity, surjectivity, and the application of the rank-nullity theorem. The conversation also emphasizes the importance of associating matrices with linear transformations relative to fixed bases.

PREREQUISITES
  • Understanding of linear transformations and isomorphisms
  • Familiarity with the rank-nullity theorem
  • Basic trigonometry for vector rotation
  • Knowledge of matrix representation of linear transformations
NEXT STEPS
  • Study the properties of linear transformations in depth
  • Learn how to apply the rank-nullity theorem in various contexts
  • Explore matrix representation of linear transformations in detail
  • Investigate the geometric interpretation of rotations in the plane
USEFUL FOR

Mathematicians, physics students, and anyone interested in linear algebra, particularly those studying transformations and their applications in 2D space.

Lauren1234
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how would I go about answering the above question I need some pointers on how to start?
 
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Hints:

You have to show that ##(1,0)## gets mapped to ##(1/2, \sqrt{3}/2)## and ##(0,1)## gets mapped to ##(-\sqrt{3}/2, 1/2)##. Showing that it is a linear isomorphism is intuitively clear: linearity is obvious, injectivity is also obvious because two different points get rotated to two different points with the same distance between them, and a rotation in the other direction over the same angle shows that your map is surjective. Of course, if you use some theorems like rank-nullity theorem you get surjectivity from injectivity or vice versa.
 
Math_QED said:
Hints:

You have to show that ##(1,0)## gets mapped to ##(1/2, \sqrt{3}/2)## and ##(0,1)## gets mapped to ##(-\sqrt{3}/2, 1/2)##. Showing that it is a linear isomorphism is intuitively clear: linearity is obvious, injectivity is also obvious because two different points get rotated to two different points with the same distance between them, and a rotation in the other direction over the same angle shows that your map is surjective. Of course, if you use some theorems like rank-nullity theorem you get surjectivity from injectivity or vice versa.
Fab I’ll give it a go. Is there a specific way I could draw the above also?
 
Lauren1234 said:
Fab I’ll give it a go. Is there a specific way I could draw the above also?

Draw? Sure. Draw the unit circle in the ##x-y##-plane. The vector ##(1,0)## is the vector both on the unit circle and on the ##x##-axis. Now, after applying the rotation it ends up at ##\pi/3## on the unit circle (60 degrees). Which vector is that? Basic trigoniometry will help here. Similarly you do the same for ##(0,1)##.
 
Math_QED said:
Draw? Sure. Draw the unit circle in the ##x-y##-plane. The vector ##(1,0)## is the vector both on the unit circle and on the ##x##-axis. Now, after applying the rotation it ends up at ##\pi/3## on the unit circle (60 degrees). Which vector is that? Basic trigoniometry will help here. Similarly you do the same for ##(0,1)##.
Yeah it says to draw the matrix bit as a carefully drawn diagram. So basically I need to draw a circle in the plane right think I’ve got you.
 
Math_QED said:
Hints:

You have to show that ##(1,0)## gets mapped to ##(1/2, \sqrt{3}/2)## and ##(0,1)## gets mapped to ##(-\sqrt{3}/2, 1/2)##. Showing that it is a linear isomorphism is intuitively clear: linearity is obvious, injectivity is also obvious because two different points get rotated to two different points with the same distance between them, and a rotation in the other direction over the same angle shows that your map is surjective. Of course, if you use some theorems like rank-nullity theorem you get surjectivity from injectivity or vice versa.
ive done this bit but I’m not exa sure what it shows does it tell me the matrix is a linear transformation?
 
Lauren1234 said:
ive done this bit but I’m not exa sure what it shows does it tell me the matrix is a linear transformation?

Do you know how to associate a matrix to a linear transformation, relative to some fixed bases?

Suppose we have a linear transformation ##T: V \to W## and ##\{e_1, \dots, e_n\}## a basis for ##V## and ##\{f_1, \dots, f_m\}## a basis for ##W##. Then you calculate ##T(e_1)## and write it in the form ##T(e_1) = \sum_{i=1}^m a_i f_i##. The coefficients ##(a_1, \dots, a_m)## come in the first column of the matrix. Similarly you calculate ##T(e_2)## to get the second column etc. The idea here is that a linear transformation is known completely if we know what the map does to a basis, so we put this information in a matrix.

In your case ##V = W = \mathbb{R}^2## and the basis for both ##V## and ##W## is ##\{(1,0), (0,1)\}##. So, you calculate ##T(1,0) ##. What coefficients do you get when you write this as a linear combination of ##(1,0)## and ##(0,1)##? These will go in the first column.
 
Math_QED said:
Do you know how to associate a matrix to a linear transformation, relative to some fixed bases?

Suppose we have a linear transformation ##T: V \to W## and ##\{e_1, \dots, e_n\}## a basis for ##V## and ##\{f_1, \dots, f_m\}## a basis for ##W##. Then you calculate ##T(e_1)## and write it in the form ##T(e_1) = \sum_{i=1}^m a_i f_i##. The coefficients ##(a_1, \dots, a_m)## come in the first column of the matrix. Similarly you calculate ##T(e_2)## to get the second column etc. The idea here is that a linear transformation is known completely if we know what the map does to a basis, so we put this information in a matrix.

In your case ##V = W = \mathbb{R}^2## and the basis for both ##V## and ##W## is ##\{(1,0), (0,1)\}##. So, you calculate ##T(1,0) ##. What coefficients do you get when you write this as a linear combination of ##(1,0)## and ##(0,1)##? These will go in the first column.
this is what I’ve done so far. Do I but them together and show they’re the same? And that means they’re a linear transformation.
8787722A-E9A4-4FCC-B188-60891F6453D6.jpeg
 
I told you what to do: Calculate ##T(1,0)## where ##T## is the rotation in your exercise.

Next, write ##T(1,0) = a (1,0) + b(0,1) = (a,b)## with ##a,b \in \mathbb{R}##. The coefficients ##(a,b)## will be the first column of your matrix.
 

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