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Lauren1234
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how would I go about answering the above question I need some pointers on how to start?
Fab I’ll give it a go. Is there a specific way I could draw the above also?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.
Lauren1234 said:Fab I’ll give it a go. Is there a specific way I could draw the above also?
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: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)##.
ive done this bit but I’m not exa sure what it shows does it tell me the matrix is a linear transformation?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.
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?
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.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.
A linear isomorphism is a bijective linear transformation between two vector spaces, which preserves the algebraic structure of the spaces. In simpler terms, it is a one-to-one and onto mapping that maintains the operations of addition and scalar multiplication between vectors.
Some key properties of a linear isomorphism include: it is bijective, meaning it has a unique inverse; it preserves linear combinations, meaning the transformation of a linear combination of vectors is equivalent to the linear combination of the transformed vectors; and it preserves the zero vector, meaning the transformation of the zero vector is still the zero vector.
To determine if a transformation is a linear isomorphism, you can check if it satisfies the properties mentioned above. Additionally, you can use the rank-nullity theorem, which states that for a linear transformation to be an isomorphism, the dimensions of the domain and codomain must be equal.
An isomorphism is a mapping between two different vector spaces, while an automorphism is a mapping from a vector space to itself. In other words, an automorphism is a special case of an isomorphism where the domain and codomain are the same vector space.
Linear isomorphisms have various applications in fields such as physics, engineering, and computer science. They can be used to solve systems of linear equations, analyze geometric transformations, and even in data compression algorithms. In essence, understanding and applying linear isomorphisms can help in solving a wide range of problems involving linear transformations.