Find Basis for Ker L & Range L | L(x,y,z,w) = (x+y, z+w, x+z)

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In summary, the basis for the kernel of L is {(1,-1,-1,1)}, the basis for the range of L is {(1,0,0),(0,1,0),(0,0,1)}, and the theorem 10.7 (dim(KerL) + dim(rangeL) = dim V) is verified.
  • #1
newtomath
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L: R^4 => R^3 is defined by L(x,y,z,w) = (x+y, z+w, x+z)

A) Find a basis for ker L

We can re write L(x,y,z,w) as x* (1,01) + y *(1,0,0) + z*(0,1,1) + w*(0,1,0).
I then reduced it to row echelon form

We now have the equations X-W=0 , Y+W=0, Z+W=0.

There are infinitely many solutions as X=W, Y= -W and Z=-W. So if we set W=1 we have

the basis for the kernel=Vector(1,-1, -1,1)


B) find a basis for range L

Given
L(x,y,z,w) = (x+y, z+w, x+z)

We can re write L(x,y,z,w) as x* (1,01) + y *(1,0,0) + z*(0,1,1) + w*(0,1,0).
S= {(1,01) ,(1,0,0) ,(0,1,1),(0,1,0)} It spans L.

To find the basis for L we set {x* (1,01) + y *(1,0,0) + z*(0,1,1) + w*(0,1,0)} = 0,0,0

I reduced it and the leading one's appear in the first 3 columns of the reduced form, the first 3 vectors in the original matrix became a basis for the range of L
They are:
Vector( {(1, (0, (1})
,
Vector( 1, 0, 0})
,
Vector( 0, 0, 1})

C) verify theorem 10.7 (dim(KerL) + dim(rangeL) = dim V

The dim can be viewed as the # of vectors in of the Ker/range.

Given (dim(KerL) + dim(rangeL) = dim V we have 1+3=4, which is the number of dimensions in the original space (L(x,y,z,w)).
 
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  • #2
newtomath said:
L: R^4 => R^3 is defined by L(x,y,z,w) = (x+y, z+w, x+z)

A) Find a basis for ker L

We can re write L(x,y,z,w) as x* (1,01) + y *(1,0,0) + z*(0,1,1) + w*(0,1,0).
I then reduced it to row echelon form

We now have the equations X-W=0 , Y+W=0, Z+W=0.

There are infinitely many solutions as X=W, Y= -W and Z=-W. So if we set W=1 we have

the basis for the kernel=Vector(1,-1, -1,1)


B) find a basis for range L

Given
L(x,y,z,w) = (x+y, z+w, x+z)

We can re write L(x,y,z,w) as x* (1,01) + y *(1,0,0) + z*(0,1,1) + w*(0,1,0).
S= {(1,01) ,(1,0,0) ,(0,1,1),(0,1,0)} It spans L.

To find the basis for L we set {x* (1,01) + y *(1,0,0) + z*(0,1,1) + w*(0,1,0)} = 0,0,0

I reduced it and the leading one's appear in the first 3 columns of the reduced form, the first 3 vectors in the original matrix became a basis for the range of L
They are:
Vector( {(1, (0, (1})
,
Vector( 1, 0, 0})
,
Vector( 0, 0, 1})

C) verify theorem 10.7 (dim(KerL) + dim(rangeL) = dim V

The dim can be viewed as the # of vectors in of the Ker/range.

Given (dim(KerL) + dim(rangeL) = dim V we have 1+3=4, which is the number of dimensions in the original space (L(x,y,z,w)).

What's your question?
 
  • #3
I forgot to type it. I believe A and B to be correct, but is my explanantion in C suffice?
 
  • #4
Sure, it's fine, and the other parts are fine also.
 

FAQ: Find Basis for Ker L & Range L | L(x,y,z,w) = (x+y, z+w, x+z)

What is the definition of "basis" in linear algebra?

In linear algebra, a basis is a set of linearly independent vectors that span a vector space. This means that any vector in the vector space can be written as a unique linear combination of the basis vectors.

How do you find the basis for the kernel of a linear transformation?

To find the basis for the kernel of a linear transformation, you need to solve the system of equations L(x,y,z,w) = 0. This means finding all possible solutions for x, y, z, and w that make the transformation equal to the zero vector. These solutions will form the basis for the kernel.

3. What is the significance of the kernel in linear algebra?

The kernel, also known as the null space, is an important concept in linear algebra because it represents all the vectors that are mapped to the zero vector by a linear transformation. This can provide insight into the behavior of the transformation and its properties.

4. How do you find the basis for the range of a linear transformation?

To find the basis for the range of a linear transformation, you need to determine all the possible outputs of the transformation for every possible input. These outputs will form the basis for the range of the transformation.

5. What is the relationship between the kernel and range of a linear transformation?

The kernel and range of a linear transformation are complementary subspaces. This means that the basis for the kernel and the basis for the range, together, form a basis for the entire vector space. Additionally, the dimension of the kernel plus the dimension of the range is equal to the dimension of the entire vector space.

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