Basis of linear transformations

In summary: Subtracting 3u from 4z, 4z- 3u= 40y_2 or 4z= 40y_2+ 3u= 40(x- y)+ 3u so that z= 10(x- y)+ (3/4)u. So we have x= 2y_2+ y, z= 10(x- y)+ (3/4)u. Replacing x and z in terms of y and u, we have L_2= <2y+ y, y, 10y+ (3/4)u, u>= <3y, y, 10y+ (3/4)u
  • #1
sid9221
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http://dl.dropbox.com/u/33103477/linear%20transformations.png

My attempt was to first find the transformed matrices L1 and L2.

L1= ---[3 1 2 -1]
-------[2 4 1 -1]

L2= ---[1 -1]
-------[1 -3]
-------[2 -8]
-------[3 -27]

Now reducing L1, I have

-------[1 0 7/10 -3/10]
-------[0 1 -1/10 -1/10]

How do I find the kernel for this and how then how do I deduce the basis ?
Also how do I find the image and then the basis for L2

If you could just give the overview of the calculations required it would be helpful.
 
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  • #2
Kernel of L1 is {x} such that L1*x=0, find all x's such that L1*x=0 by mechanically solving the equation. Believe me there aren't that many other than x=[0,0,0,0]'.

Image of L2 is even simpler, {y} such that y=L2*x for some x, so that {y} is the span of columns of L2.
 
  • #3
[itex]L_1[/itex] is defined by [itex]L_1(x_1, x_2, x_3 x_4)= (3x_1+ x_2+ 2x_3- x_4, 2x_1+ 4x_2+ x_3- x_4)[/itex]. The "kernel" of [itex]L_1[/itex], also called its "nullspace" is defined as the set or all [itex](x_1, x_2, x_3, x_4)[/itex] that are mapped into (0, 0). In other words, we must have [itex]3x_1+ x_2+ 2x_3- x_4= 0[/itex] and [itex]2x_1+ 4x_2+ x_3- x_4= 0[/itex]. Those are two equations in 4 variables- we can solve for two of them in terms of the other two.

Since both equations have "[itex]-x_4[/itex]", we can subtract one equation form the other to get [itex]x_1- 3x_2+ x_3= 0[/itex] or [itex]x_1= 3x_2- x_3[/itex]. Replacing [itex]x_1[/itex] by that in either of the two equations, we will have an equation involving only [itex]x_2[/itex], [itex]x_3/itex], and [itex]x_4[/itex]. We can solve that for [itex]x_4[/itex] in terms of [itex]x_2[/itex] and [itex]x_4[/itex] and so have a two dimensional space in terms of those two parameters.

[itex]L_2[/itex] is defined by [itex]L(y_1,y_2)= (y_1- y_2, y_1- 3y_2, 2y_1- 8y_2, 3y_1- 27y_2)[/itex]

If we call that "<x, y, z, u>" we have [itex]x= y_1- y_2[/itex], [itex]y= y_1- 3y_2[/itex], [itex]z= 2y_1- 8y_2[/itex], and [itex]u= 3y_1- 27y_2[/itex]. Now we want to get relations among x, y, z, and u only. Subtracting y from x, x- y= 2y_2. Subtracting 2u from 3z, 3z- 2u= 30y_2= 14(x- y). That gives 3z- 2u= 14x- 14y so that 3z= 14x- 14y+ 2u or z= (14/3)x- (14/3)y+ (2/3)u.
 
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FAQ: Basis of linear transformations

What is a linear transformation?

A linear transformation is a mathematical operation that maps one vector space to another. It preserves the basic structure of the vector space, such as addition and scalar multiplication.

What is the basis of a linear transformation?

The basis of a linear transformation is a set of vectors that span the vector space being transformed. These vectors serve as the foundation for all possible combinations of the transformation.

Why is the basis of a linear transformation important?

The basis of a linear transformation helps us understand the behavior of the transformation and how it affects the vector space. It also allows us to represent the transformation using matrices, making it easier to analyze and manipulate.

Can a linear transformation have more than one basis?

Yes, a linear transformation can have multiple bases. This is because the basis of a transformation is not unique and there can be many different sets of vectors that span the same vector space.

How do you find the basis of a linear transformation?

To find the basis of a linear transformation, we can use the concept of linear independence. A basis is a set of linearly independent vectors, meaning they are not redundant and cannot be expressed as a linear combination of other vectors in the set. We can also use the null space of the transformation to find its basis.

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