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maths 1B last minute questions (2 Viewers)

Drsoccerball

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1) How can you determine the nullity of this transformation without finding the matrix? Also does P_2 -> P_3 mean that we put in a polynomial of degree 2 and we get out one of degree 3 ?



2) Let A be a fixed 3 x 3 matrix and define a linear map T: M_{33} -> M_{33} by T(x) = AX. If lambda is a real eigenvalue of T corresponding to an invertible eigenvector X, find lambda in terms of det(A).

3) Let T be a linear map which reflects vectors in R^2 about the line y = x.

a) Explain why <1,1> and <1,-1> are eigenvectors and give their corresponding eigenvalues.
 

InteGrand

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1) How can you determine the nullity of this transformation without finding the matrix? Also does P_2 -> P_3 mean that we put in a polynomial of degree 2 and we get out one of degree 3 ?



2) Let A be a fixed 3 x 3 matrix and define a linear map T: M_{33} -> M_{33} by T(x) = AX. If lambda is a real eigenvalue of T corresponding to an invertible eigenvector X, find lambda in terms of det(A).

3) Let T be a linear map which reflects vectors in R^2 about the line y = x.

a) Explain why <1,1> and <1,-1> are eigenvectors and give their corresponding eigenvalues.
3) Remember, geometrically, eigenvectors are those special directions (really vectors) that only get scaled by T, without having their direction changed (a typical vector won't have this property as it would normally get rotated a bit too). And the factor an eigenvector gets scaled by (which is negative if the vector gets flipped in direction too) is the eigenvalue for that eigenvector.

Take a look at this Mona Lisa picture to get a feel for this (and read the picture description): https://en.wikipedia.org/wiki/Eigen...rs#/media/File:Mona_Lisa_eigenvector_grid.png . Basically a linear map is applied to the picture, and the blue vector direction turns out to be unchanged (so vectors lying in the line of the blue direction are eigenvectors), whilst this is not the case for the red one (since that changes direction when acted upon by the linear map).

Now to the question at hand. Clearly the vectors <1,1> and <1,-1>, when flipped about the line y = x, don't change their line direction (one of them flips around but this is just scaling by -1). This is because <1,1> stays as is (since it is on the line y = x), whilst the vector <1, -1> just flips about the origin (gets negated). So <1,1> is an eigenvector with eigenvalue 1 (since T(<1,1>) = <1,1>, since it's unchanged), and <1,-1> is an eigenvector with eigenvalue -1 (since it gets flipped, i.e. T(<1,-1>) = –<1,-1>).
 

Drsoccerball

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3) Remember, geometrically, eigenvectors are those special directions (really vectors) that only get scaled by T, without having their direction changed (a typical vector won't have this property as it would normally get rotated a bit too). And the factor an eigenvector gets scaled by (which is negative if the vector gets flipped in direction too) is the eigenvalue for that eigenvector.

Take a look at this Mona Lisa picture to get a feel for this (and read the picture description): https://en.wikipedia.org/wiki/Eigen...rs#/media/File:Mona_Lisa_eigenvector_grid.png . Basically a linear map is applied to the picture, and the blue vector direction turns out to be unchanged (so vectors lying in the line of the blue direction are eigenvectors), whilst this is not the case for the red one (since that changes direction when acted upon by the linear map).

Now to the question at hand. Clearly the vectors <1,1> and <1,-1>, when flipped about the line y = x, don't change their line direction (one of them flips around but this is just scaling by -1). This is because <1,1> stays as is (since it is on the line y = x), whilst the vector <1, -1> just flips about the origin (gets negated). So <1,1> is an eigenvector with eigenvalue 1 (since T(<1,1>) = <1,1>, since it's unchanged), and <1,-1> is an eigenvector with eigenvalue -1 (since it gets flipped, i.e. T(<1,-1>) = –<1,-1>).
I was looking for this everywhere.
 

InteGrand

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1) How can you determine the nullity of this transformation without finding the matrix? Also does P_2 -> P_3 mean that we put in a polynomial of degree 2 and we get out one of degree 3 ?



2) Let A be a fixed 3 x 3 matrix and define a linear map T: M_{33} -> M_{33} by T(x) = AX. If lambda is a real eigenvalue of T corresponding to an invertible eigenvector X, find lambda in terms of det(A).

3) Let T be a linear map which reflects vectors in R^2 about the line y = x.

a) Explain why <1,1> and <1,-1> are eigenvectors and give their corresponding eigenvalues.
 
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InteGrand

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1) How can you determine the nullity of this transformation without finding the matrix? Also does P_2 -> P_3 mean that we put in a polynomial of degree 2 and we get out one of degree 3 ?



2) Let A be a fixed 3 x 3 matrix and define a linear map T: M_{33} -> M_{33} by T(x) = AX. If lambda is a real eigenvalue of T corresponding to an invertible eigenvector X, find lambda in terms of det(A).

3) Let T be a linear map which reflects vectors in R^2 about the line y = x.

a) Explain why <1,1> and <1,-1> are eigenvectors and give their corresponding eigenvalues.






 
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seanieg89

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What's with the dx's? lol.

And are the a_k real numbers? If so, the answer is no (consider the alternating harmonic series).

If the a_k are positive real numbers, the answer is yes.
 

seanieg89

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All these identities that I don't know :'( rip
If you are approaching these kinds of facts as identities to memorise, you are going to make life hard for yourself as there are too many to count!

Instead, when you learn about a new object/operation, make sure that the definition is really ingrained in your head (multiple equivalent definitions if possible, and understand why they are equivalent). In this case, it should be fairly clear from any definition of the determinant that multiplying a row/column of the matrix by a constant multiplies the determinant by that constant. So multiplying the entire matrix by that constant multiplies the determinant by c^n.

This can also be seen as a consequence of det(AB)=det(A)det(B), which is perhaps the most important determinant identity to know. This identity is less obvious using some definitions of det than others.

In general, when you are introduced to a new mathematical object, it is a valuable exercise to try to guess/deduce/prove properties of it yourself before diving into doing computational questions.
 

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